{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d41e09f2",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "from toolz import *\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.special import expit\n",
    "\n",
    "from linearmodels.panel import PanelOLS\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f21c36",
   "metadata": {},
   "source": [
    "# 24 - The Difference-in-Differences Saga\n",
    " \n",
    "After discussing treatment effect heterogeneity, we will now switch gears a bit, back into average treatment effects. Over the next few chapters, we will cover some recent developments in panel data methods. A panel is a data structure that has repeated observations across time. The fact that we observe the same unit in multiple time periods allows us to see what happens before and after a treatment takes place. This makes panel data a promising alternative to identifying the causal effects when randomization is not possible.\n",
    " \n",
    "To motivate the use of panel data, we will mostly talk about causal inference applications to marketing. Marketing is particularly interesting for its notorious difficulty in running randomized experiments. In marketing, we often can't control who receives the treatment, that is, who sees our advertisements. When a new user comes to our site or downloads our app, we have no good way of knowing if that user came because he or she saw one of our campaigns or due to some other factor. \n",
    " \n",
    "(OBS: For those more familiar with marketing attribution, I'm aware of the many attribution tools that aim at solving this problem. But I'm also aware of their many limitations). \n",
    " \n",
    "The problem is even bigger with offline marketing. How can you know if a TV campaign brought value in excess of its costs? Because of that, a common practice in marketing is Geo-Experiments: we deploy a marketing campaign to some geographical region but not others and compare them. In this design panel data methods are particularly interesting: we can collect data on an entire geography (unit) across multiple periods of time. To make sense of this sort of data and identify the causal effect, perhaps the most popular methods are those in the Diff-in-Diff (DiD) family. \n",
    " \n",
    "The years 2020 and 2021 have not been easy for most of us. But it was particularly hard on DiD. A lot of recent research highlighted some severe flaws in these methods, flaws which were not well known in the past. So, although we already have a chapter in Part I covering DiD, the content there is rather introductory. It doesn’t cover the new findings and effervescent discussions around panel data methods. We should now take a more thorough look at them, beginning with Diff-in-Diff. In this specific chapter, I’ll try to summarize the recently discovered problems with diff-in-diff and also show how to fix them. This chapter is divided into three sections:\n",
    " \n",
    "1. **Birth**: Recap why panel data is so attractive for causal inference and how DiD and Two Way Fixed Effect (TWFE) leverages the temporal structure in its favor. \n",
    "2. **Death**: Digest one key assumption implied by DiD and TWFE models that has been overlooked. Understand when and how that assumption can fail.  \n",
    "3. **Enlightenment**: Knowing the problem with DiD and TWFE, we can now think about a solution for it. This part shows a simple workaround to the problem explored in section 2. \n",
    " \n",
    "Let’s get right into it!\n",
    " \n",
    " \n",
    "## 1) Birth: The Promise of Panel Data\n",
    " \n",
    "![img](data/img/did-saga/promise.png)\n",
    " \n",
    "Like I’ve said, panel data is when we have multiple units `i` over multiple periods of time `t`. Think about a policy evaluation scenario in the US, where you want to check the effect of cannabis legalization on crime rate. You have crime rate data on multiple states `i` over multiple time periods `t`. You also observe at what point in time each state adopts legislation in the direction of cannabis legalization. I hope you can see why this is incredibly powerful for causal inference. Call cannabis legalization the treatment `D` (since `T` is taken; it represents time). We can follow the trend on crime rates for a particular state that eventually gets treated and see if there are any disruptions in the trend at the treatment time. In a way, a state serves as its own control unit, in a before and after comparison. Furthermore, because we have multiple states, we can also compare treated states to control states. When we put both comparisons together, treated vs control and before vs after treatment, we end up with an incredibly powerful tool to infer counterfactuals and, hence, causal effects. \n",
    " \n",
    "Panel data methods are often used in government policy evaluation, but we can easily make an argument about why they are also incredibly useful for the (tech) industry. Companies often track user data across multiple periods of time, which results in a rich panel data structure. Not only that, sometimes experimentation is not possible, so we have to rely on other identification strategies. To explore that idea further, let's consider a hypothetical example of a young tech company that tracks the number of people that installed its app across multiple cities. At some point in 2021, the tech company launched a new feature in their app. It now wants to know how many new uses that feature brought to the company. The rollout was gradual. Some cities got the feature in `2021-06-01`. Others, in `2021-07-15`. The full rollout to the rest of the cities only happens in 2022. Since our data only goes up until `2021-07-31`, this last group can be considered the control group. In causal inference terms, rolling out this feature can be seen as the treatment and number of installs can be seen as the outcome. We want to know the treatment effect on the outcome, that is, the effect of the new feature on the number of installs. \n",
    " \n",
    "Notice how the tech company cannot do an experiment here. They can’t control which person gets to know they have a new feature. We say that they have limited control over the treatment assignment. That's because the unit of analysis is **people that are not yet their customers**. They want to know how many of those they can convert to customers by installing their app. Of course they can’t randomize anything for those people. So, instead, they changed the unit of analysis to cities. The release in one city versus the other is something they can control, which is not the case for the release for one person versus the other.\n",
    " \n",
    "The group of cities that got the feature (got treated) at a specific point in time is called a cohort. In our case, we have three cohorts: one that got treated in `2021-06-01`, another that got treated in `2021-07-15` and a control cohort, which only gets treated after the end of our data. To get a sense of what this data looks like, let's plot the average daily installs grouped by cohort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "537f029d",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "date = pd.date_range(\"2021-05-01\", \"2021-07-31\", freq=\"D\")\n",
    "cohorts = pd.to_datetime([\"2021-06-01\", \"2021-07-15\", \"2022-01-01\"]).date\n",
    "units = range(1, 100+1)\n",
    "\n",
    "np.random.seed(1)\n",
    "\n",
    "df = pd.DataFrame(dict(\n",
    "    date = np.tile(date, len(units)),\n",
    "    unit = np.repeat(units, len(date)),\n",
    "    cohort = np.repeat(np.random.choice(cohorts, len(units)), len(date)),\n",
    "    unit_fe = np.repeat(np.random.normal(0, 5, size=len(units)), len(date)),\n",
    "    time_fe = np.tile(np.random.normal(size=len(date)), len(units)),\n",
    "    week_day = np.tile(date.weekday, len(units)),\n",
    "    w_seas = np.tile(abs(5-date.weekday) % 7, len(units)),\n",
    ")).assign(\n",
    "    trend = lambda d: (d[\"date\"] - d[\"date\"].min()).dt.days/70,\n",
    "    day = lambda d: (d[\"date\"] - d[\"date\"].min()).dt.days,\n",
    "    treat = lambda d: (d[\"date\"] >= d[\"cohort\"]).astype(int),\n",
    ").assign(\n",
    "    y0 = lambda d: 10 + d[\"trend\"] + d[\"unit_fe\"] + 0.1*d[\"time_fe\"] + d[\"w_seas\"]/10,\n",
    ").assign(\n",
    "    y1 = lambda d: d[\"y0\"] + 1\n",
    ").assign(\n",
    "    tau = lambda d: d[\"y1\"] - d[\"y0\"],\n",
    "    installs = lambda d: np.where(d[\"treat\"] == 1, d[\"y1\"], d[\"y0\"])\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b2c6efd5",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "[plt.vlines(x=cohort, ymin=9, ymax=15, color=color, ls=\"dashed\") for color, cohort in zip([\"C0\", \"C1\"], cohorts[:-1])]\n",
    "sns.lineplot(\n",
    "    data=(df\n",
    "          .groupby([\"cohort\", \"date\"])[\"installs\"]\n",
    "          .mean()\n",
    "          .reset_index()),\n",
    "    x=\"date\",\n",
    "    y = \"installs\",\n",
    "    hue=\"cohort\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "923d605f",
   "metadata": {},
   "source": [
    "The dashed lines mark the moment a cohort got the treatment (the feature was rolled out). Take a moment to appreciate the richness of the data depicted in the above plot. First, we can see that each cohort has its own baseline level. That's simply because different cities have different population sizes, leading to more or less installs depending on the city size. For instance, it looks like cities in the first cohort (treated in 06/01) have a higher baseline, compared to the cities in the other cohorts. Also, it looks like the control cohort has a lower baseline of intalls. This means that simply comparing treated cohorts against the control cohorts would yield a biased result, since $Y_{0}$ for the control is lower than the $Y_{0}$ for the treated, or $Y_{0}|G=Control < Y_{0}|G=Treated$, where we use $G$ to denote the cohort. Fortunately this won’t be a problem. Panel data allow us to compare across cities **and** time, which allows it to adjust for different baselines.\n",
    " \n",
    "Speaking of time, notice how there is an overall upward trend with some wiggles (which looks like weekly seasonality). Focusing on the control cohort, it looks like daily installs went from about 10 in May to about 11 in June, an increase of about 1. In technical terms, latter time periods have higher $Y_{0}$ than early time periods. This means that simply comparing the same cities across time would also yield biased results. Once again, we are fortunate that the panel data structure allows us to compare not only across time, but also across cities, adjusting for trends.\n",
    " \n",
    "Ideally, to infer the effect of the rollout of this new feature on installs, we want to know what would have happened to the cohorts that got the feature, had they not got it. We want to estimate the counterfactual outcome $Y(0)$ in the post treatment periods for the treated cohorts. If we denote each cohort by the time it got treated `g` (remember that a cohort is just a group of cities that got treated at the same time), we can write this counterfactual as $E[Y_0|t\\geq g]$, which would then allow us to define the treatment effect on the treated (the ATT) for cohort `g` as follows:\n",
    " \n",
    "$$\n",
    "E[Y_1|t \\geq g] - E[Y_0|t\\geq g]\n",
    "$$\n",
    " \n",
    " \n",
    "The next question is how can we estimate this from the data we have. Well, one way is to exploit the power of the panel data structure and estimate those counterfactuals. For instance, we could use linear regression and the Diff-in-Diff formulation to get a Two Way Fixed Effect model. Let's say each city `i` has a base install level $\\gamma_i$. This ties back to what we saw earlier: maybe a city has more installs because it has a bigger population or because its culture is more in line with the product from our tech company. Regardless of the reason and even if we don't know why, we say that those unit idiosyncrasies can be captured by a **time fixed parameter** $\\gamma_i$. Similarly, we can say that each time period `t` has a baseline install level which we can capture by a **unit fixed parameter** $\\theta_t$. If that is the case, a good way of modeling install is to say it depends on the city (unit) effect $\\gamma$ and the time effect $\\theta$, plus some random noise.\n",
    " \n",
    "$$\n",
    "Installs_{it} = \\gamma_i + \\theta_t + e_{it}\n",
    "$$\n",
    " \n",
    "To include the treatment in this picture, let's define a variable $D_{it}$ which is 1 if the unit is treated and zero otherwise. In our example, this variable would be always zero for the never treated cohort. It would also be zero for all the other cohorts at the beginning, but it would turn into 1 at 06/01 for the cohort treated on 06/01 and stay at 1 after that. Also, it would turn into 1 at 07/15 for the cohort treated on 07/15. We can include those treatment indicators in our model of installs as follows:\n",
    " \n",
    "$$\n",
    "Installs_{it} = \\tau D_{it} + \\gamma_i + \\theta_t + e_{it}\n",
    "$$\n",
    " \n",
    "Estimating the above model with OLS is what is called the Two-Way Fixed Effects Model (TWFE). Notice that $\\tau$ would be the treatment effect, as it tells us how much installs changes once units are treated.\n",
    " \n",
    "Another way of looking at it is to invoke the \"holding things constant\" propriety of linear regression. If we estimate the above model, we could read the estimate of $\\tau$ as how much installs would change if we flip the treatment from 0 to 1 while holding the unit `i` and time `t` fixed.\n",
    " \n",
    "Notice how bold this is! To say we would hold each unit fixed while seeing how $D$ changes the outcome is to say we are controlling for all unit specific characteristics that are constant through time, known and unknown. For example, we would be controlling for cities' baseline install level, which we can measure, but also stuff we have no idea about, like how much a city culture is in line with our product. The only requirement is that this characteristic is fixed over the time of the analysis. Moreover, to say we would hold each time period fixed is to say we are controlling for all year specific characteristics. For instance, since we are holding the year fixed, while looking at the effect of $D$, we would be controlling for the trend and seasonalities we saw earlier.\n",
    " \n",
    "To see all this power in action all we have to do is run an OLS model with the treatment indicator $D$ (`treat` here), plus dummies for the units and time. In our particular example, I've generated data in such a way that the effect of the treatment (new feature) is to increase installs by 1. Notice how TWFE can recover that treatment effect perfectly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ba440e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0000000000000533"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = f\"\"\"installs ~ treat + C(unit) + C(date)\"\"\"\n",
    "\n",
    "twfe_model = smf.ols(formula, data=df).fit()\n",
    "\n",
    "twfe_model.params[\"treat\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d377ab60",
   "metadata": {},
   "source": [
    "Since I've simulated the data above, I know exactly the true individual treatment effect, which is stored in the `tau` column. Since the TWFE should recover the treatment effect on the treated, we can verify that the true ATT matches the one estimated above. All we have to do is filter the treated units and period (`treat==1`) and take the average of the `tau` column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "26ea6874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"treat==1\")[\"tau\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "529ecc1c",
   "metadata": {},
   "source": [
    "Before anyone comes and says that generating one dummy column for each unit is impossible with big data, let me come forward and tell you that, yes, that is true. But there is an easy work around. We can use the FWL theorem to partition that single regression into two. In fact, running the above model is numerically equivalent to estimating the following model\n",
    " \n",
    "$$\n",
    "\\tilde{Installs}_{it} = \\tau \\tilde D_{it} + e_{it}\n",
    "$$\n",
    " \n",
    "where \n",
    " \n",
    "$$\n",
    "\\tilde{Installs}_{it} = Installs_{it} - \\underbrace{\\frac{1}{T}\\sum_{t=0}^T Installs_{it}}_\\text{Time Average} - \\underbrace{\\frac{1}{N}\\sum_{i=0}^N Installs_{it}}_\\text{Unit Average}\n",
    "$$\n",
    " \n",
    "and\n",
    " \n",
    "$$\n",
    "\\tilde{D}_{it} = D_{it} - \\frac{1}{T}\\sum_{t=0}^T D_{it} - \\frac{1}{N}\\sum_{i=0}^N D_{it}\n",
    "$$\n",
    " \n",
    "In words now, in case the math is too crowded, we subtract the unit average across time (first term) and the time average across units (second term) from both the treatment indicator and the outcome variable to constrict the residuals. This process is oftentimes called de-meaning, since we subtract the mean from the outcome and treatment. Finally, here is the same exact thing, but in code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5dd0e3eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -11.4580</td> <td> 7.98e-16</td> <td>-1.44e+16</td> <td> 0.000</td> <td>  -11.458</td> <td>  -11.458</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>treat</th>     <td>    1.0000</td> <td> 2.02e-15</td> <td> 4.96e+14</td> <td> 0.000</td> <td>    1.000</td> <td>    1.000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@curry\n",
    "def demean(df, col_to_demean):\n",
    "    return df.assign(**{col_to_demean: (df[col_to_demean]\n",
    "                                        - df.groupby(\"unit\")[col_to_demean].transform(\"mean\")\n",
    "                                        - df.groupby(\"date\")[col_to_demean].transform(\"mean\"))})\n",
    "\n",
    "\n",
    "formula = f\"\"\"installs ~ treat\"\"\"\n",
    "mod = smf.ols(formula,\n",
    "              data=df\n",
    "              .pipe(demean(col_to_demean=\"treat\"))\n",
    "              .pipe(demean(col_to_demean=\"installs\")))\n",
    "\n",
    "result = mod.fit()\n",
    "\n",
    "result.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8748af77",
   "metadata": {},
   "source": [
    "Another thing we can do to understand what TWFE model is doing is to plot the counterfactual predictions $\\hat{Y_0}|t \\geq g$. This is helpful because what our model sees as the treatment effect $\\hat{\\tau}$ as simply the estimated difference $Y_1 - \\hat{Y_0}$. Looking at this explicit difference can shed some light on what the model is doing. In the plot below, we can see exactly that, $\\hat{Y_0}$ represented by dotted lines. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea26ec52",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pred = df.assign(**{\"installs_hat_0\": twfe_model.predict(df.assign(**{\"treat\":0}))})\n",
    "          \n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "[plt.vlines(x=cohort, ymin=9, ymax=15, color=color, ls=\"dashed\") for color, cohort in zip([\"C0\", \"C1\"], cohorts[:-1])]\n",
    "sns.lineplot(\n",
    "    data=(df_pred\n",
    "          .groupby([\"cohort\", \"date\"])[\"installs_hat_0\"]\n",
    "          .mean()\n",
    "          .reset_index()),\n",
    "    x=\"date\",\n",
    "    y = \"installs_hat_0\",\n",
    "    hue=\"cohort\",\n",
    "    alpha=0.7,\n",
    "    ls=\"dotted\",\n",
    "    legend=None\n",
    ")\n",
    "sns.lineplot(\n",
    "    data=(df_pred\n",
    "          .groupby([\"cohort\", \"date\"])[\"installs\"]\n",
    "          .mean()\n",
    "          .reset_index()),\n",
    "    x=\"date\",\n",
    "    y = \"installs\",\n",
    "    hue=\"cohort\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d56ff6bd",
   "metadata": {},
   "source": [
    "This plot shows us how TWFE is projecting the trend it sees in the control units onto the treated units and it is also adjusting the levels. For example, if we look at the red cohort, the counterfactual $Y_0$ is the average trend from the blue and purple cohorts (trend projection) but shifted to the level of the red cohort (level adjustment). This is why we see TWFE as a Difference-in-Differences method. It also does the trend projection and level adjustment, but it works for multiple time periods and multiple units (in the 2 units by 2 period case, TWFE and DiD are equivalent)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37064ff3",
   "metadata": {},
   "source": [
    "## 2) Death: Failures over Effect Heterogeneity\n",
    " \n",
    " \n",
    "As we just saw, DiD and TWFE has its merits. It can estimate counterfactuals quite well, accommodating both time and unit specific variations. This makes it a powerful causal inference technique. But if this was all there was to it, we wouldn't need this chapter, as this is very much covered in Part I of this book. What happened recently is that many academics noticed that extending the 2 by 2 DiD to multiple periods with TWFE is not as straightforward as we first thought. In fact, **TWFE, in its usual formulation, turns out to be biased in many real life applications**. This event caused a wave of revisions in multiple studies in economics that relied on this technique. To understand all of that, the best way to start is by stating the underlying assumption.\n",
    "\n",
    "![img](data/img/did-saga/death.png)\n",
    " \n",
    "For simplicity sake let's consider the FE model without the time effects:\n",
    " \n",
    "$$\n",
    "y_{it} = \\tau D_{it} + \\gamma_i + e_{it}\n",
    "$$\n",
    " \n",
    " \n",
    "We can group the assumption this model makes into two groups\n",
    " \n",
    "1. Functional Form Assumptions:\n",
    "    * No heterogeneous effects in time (constant effects);\n",
    "    * Linearity in the covariates;\n",
    "    * Additive fixed effects.\n",
    "2. Strict Exogeneity\n",
    "    * Parallel trends\n",
    "    * No anticipation\n",
    "    * No unobserved time varying confounders\n",
    "    * Past treatment don't affect current outcome (no carryover)\n",
    "    * Past outcome don't affect current treatment (no feedback)\n",
    "    \n",
    "Here, we will stick to the functional form assumptions. Linearity in the covariates is very well known and applies to all linear regression models. But, as we saw with the chapter on Double/Debiased Machine Learning, we can easily relax that assumption with a machine learning model. This means that we can relax this assumption if we wish to do so. As for the additive fixed effect, this is not a too restrictive assumption, so it doesn't cause a lot of problems. The one I want to focus on (and which generated a lot of fuss) is the one about no heterogeneous effects in time.\n",
    " \n",
    "### Treatment Effect Heterogeneity in Time\n",
    " \n",
    "If you ever worked with marketing or tech, you know things take time to mature. If you launch a new feature, it will take time for users to get used to it. Similarly, if you start a marketing campaign, the effect of that campaign won't be instantaneous. It will mature over time and perhaps bring new users even after the campaign is over. This is **not** the pattern that we had in install data we've seen earlier. There, installs jumped up instantaneously, at the moment the cohort is treated. What happens if we change that to be more in line with what we see in reality. Namely, let's make it so that the ATT is still 1, but now, it takes 10 days to mature (so it will be 0.1 at the first treatment day, 0.2 at the second treatment day and so on, until it reaches 1 on the 10th day). Also, I'll reduce the size of the time and unit effects so that the overall trend is easier to see. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3e976be",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "date = pd.date_range(\"2021-05-01\", \"2021-07-31\", freq=\"D\")\n",
    "cohorts = pd.to_datetime([\"2021-06-01\", \"2021-07-15\", \"2022-01-01\"]).date\n",
    "units = range(1, 100+1)\n",
    "\n",
    "np.random.seed(1)\n",
    "\n",
    "df_heter = pd.DataFrame(dict(\n",
    "    date = np.tile(date.date, len(units)),\n",
    "    unit = np.repeat(units, len(date)),\n",
    "    cohort = np.repeat(np.random.choice(cohorts, len(units)), len(date)),\n",
    "    unit_fe = np.repeat(np.random.normal(0, 5, size=len(units)), len(date)),\n",
    "    time_fe = np.tile(np.random.normal(size=len(date)), len(units)),\n",
    "    week_day = np.tile(date.weekday, len(units)),\n",
    "    w_seas = np.tile(abs(5-date.weekday) % 7, len(units)),\n",
    ")).assign(\n",
    "    trend = lambda d: (d[\"date\"] - d[\"date\"].min()).dt.days/70,\n",
    "    day = lambda d: (d[\"date\"] - d[\"date\"].min()).dt.days,\n",
    "    treat = lambda d: (d[\"date\"] >= d[\"cohort\"]).astype(int),\n",
    ").assign(\n",
    "    y0 = lambda d: 10 + d[\"trend\"] + 0.2*d[\"unit_fe\"] + 0.05*d[\"time_fe\"] + d[\"w_seas\"]/50,\n",
    ").assign(\n",
    "    y1 = lambda d: d[\"y0\"] + np.minimum(0.1*(np.maximum(0, (d[\"date\"] - d[\"cohort\"]).dt.days)), 1)\n",
    ").assign(\n",
    "    tau = lambda d: d[\"y1\"] - d[\"y0\"],\n",
    "    installs = lambda d: np.where(d[\"treat\"] == 1, d[\"y1\"], d[\"y0\"])\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "52001ad3",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "[plt.vlines(x=cohort, ymin=9, ymax=15, color=color, ls=\"dashed\") for color, cohort in zip([\"C0\", \"C1\"], cohorts)]\n",
    "sns.lineplot(\n",
    "    data=(df_heter\n",
    "          .groupby([\"cohort\", \"date\"])[\"installs\"]\n",
    "          .mean()\n",
    "          .reset_index()),\n",
    "    x=\"date\",\n",
    "    y = \"installs\",\n",
    "    hue=\"cohort\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e26ad4b8",
   "metadata": {},
   "source": [
    "What we see is that the installs still reach the same level as they did before, but it takes some time (10 days) for that. This seems reasonable right? Most of the data we see in real life works like that, with effects taking some time to mature. Ok, so let's run TWFE model on this data to see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de13959f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated Effect:  0.7867708225724858\n",
      "True Effect:  0.8544117647058823\n"
     ]
    }
   ],
   "source": [
    "formula = f\"\"\"installs ~ treat + C(date) + C(unit)\"\"\"\n",
    "\n",
    "twfe_model = smf.ols(formula, data=df_heter).fit()\n",
    "\n",
    "print(\"Estimated Effect: \", twfe_model.params[\"treat\"])\n",
    "print(\"True Effect: \", df_heter.query(\"treat==1\")[\"tau\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f2c130c",
   "metadata": {},
   "source": [
    "First, notice that the true ATT is no longer 1. That is because it will be smaller in the first few periods. Second, and most importantly, we can see is that the **estimated ATT from TWFE is not recovering the true ATT** anymore. To put it simply:TWFE is biased. But why is that? We have parallel trends, no anticipation and all the other strict exogeneity assumptions here. So what is going on?\n",
    " \n",
    "The first step towards understanding what is happening is to realize that TWFE can actually be decomposed into multiple 2 by 2 Diff-in-Diffs. In our example, that would be one that compares early treated to never treated, late treated against never treated, early treated against late treated (with late treated serving as the control) and late treated against early treated (with early treated being the control):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "855b6381",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g_plot_data = (df_heter\n",
    "               .groupby([\"cohort\", \"date\"])[\"installs\"]\n",
    "               .mean()\n",
    "               .reset_index()\n",
    "               .astype({\"cohort\":str}))\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(15,8), sharex=True, sharey=True)\n",
    "\n",
    "def plot_comp(df, ax, exclude_cohort, name):\n",
    "    \n",
    "    palette=dict(zip(map(str, cohorts), [\"C0\", \"C1\", \"C2\"]))\n",
    "    \n",
    "    sns.lineplot(\n",
    "        data=df.query(f\"cohort != '{exclude_cohort}'\"),\n",
    "        x=\"date\",\n",
    "        y=\"installs\",\n",
    "        hue=\"cohort\",\n",
    "        palette=palette,\n",
    "        legend=None,\n",
    "        ax=ax\n",
    "    )\n",
    "\n",
    "    sns.lineplot(\n",
    "        data=df.query(f\"cohort == '{exclude_cohort}'\"),\n",
    "        x=\"date\",\n",
    "        y = \"installs\",\n",
    "        hue=\"cohort\",\n",
    "        palette=palette,\n",
    "        alpha=0.2,\n",
    "        legend=None,\n",
    "        ax=ax\n",
    "    )\n",
    "    \n",
    "    ax.set_title(name)\n",
    "\n",
    "plot_comp(g_plot_data, axs[0,0], cohorts[1], \"Early vs Never\")\n",
    "plot_comp(g_plot_data, axs[0,1], cohorts[0], \"Late vs Never\")\n",
    "\n",
    "plot_comp(g_plot_data[g_plot_data[\"date\"]<=cohorts[1]], axs[1,0], cohorts[-1], \"Early vs Late\")\n",
    "plot_comp(g_plot_data[g_plot_data[\"date\"]>cohorts[0]], axs[1,1], cohorts[-1], \"Late vs Early\")\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6911ad",
   "metadata": {},
   "source": [
    "The first three comparisons are no reason for concern, mostly because what they use as control is very well behaved. However, the fourth comparison, late vs early, is problematic. Notice that this comparison uses the early treated as a control. Also notice that this early treated control has a weird behavior. It climbs up sharply at the beginning. That is a reflection of our ATT not being instantaneous, but instead taking 10 days to mature. Intuitively, we can see that this will mess up the estimation of the counterfactual trend in the DiD, making it steeper than it should be. To visualize that, let's plot the estimated counterfactual $Y_0$ for the late treated in this 4th group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6fcd0fe",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "late_vs_early = (df_heter\n",
    "                 [df_heter[\"date\"].astype(str)>=\"2021-06-01\"]\n",
    "                 [lambda d: d[\"cohort\"].astype(str)<=\"2021-08-01\"])\n",
    "\n",
    "\n",
    "formula = f\"\"\"installs ~ treat + C(date) + C(unit)\"\"\"\n",
    "\n",
    "twfe_model = smf.ols(formula, data=late_vs_early).fit()\n",
    "\n",
    "late_vs_early_pred = (late_vs_early\n",
    "                      .assign(**{\"installs_hat_0\": twfe_model.predict(late_vs_early.assign(**{\"treat\":0}))})\n",
    "                      .groupby([\"cohort\", \"date\"])\n",
    "                      [[\"installs\", \"installs_hat_0\"]]\n",
    "                      .mean()\n",
    "                      .reset_index())\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.title(\"Late vs Early Counterfactuals\")\n",
    "sns.lineplot(\n",
    "    data=late_vs_early_pred,\n",
    "    x=\"date\",\n",
    "    y = \"installs\",\n",
    "    hue=\"cohort\",\n",
    "    legend=None\n",
    ")\n",
    "\n",
    "sns.lineplot(\n",
    "    data=(late_vs_early_pred\n",
    "          [late_vs_early_pred[\"cohort\"].astype(str) == \"2021-07-15\"]\n",
    "          [lambda d: d[\"date\"].astype(str) >= \"2021-07-15\"]\n",
    "         ),\n",
    "    x=\"date\",\n",
    "    y =\"installs_hat_0\",\n",
    "    alpha=0.7,\n",
    "    color=\"C0\",\n",
    "    ls=\"dotted\",\n",
    "    label=\"counterfactual\"\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bff9cde7",
   "metadata": {},
   "source": [
    "Just like we said, the counterfactuals have a much steeper trend than it should have. It is capturing that quick increase in the beginning of the early treatment and projecting that trend onto the late treated. \n",
    " \n",
    "More technically, it can be shown (Goodman-Bacon, 2019) that, even under strict exogeneity (parallel trends, no anticipation...), if the cohorts have the same size, the TWFE estimator will converge to\n",
    " \n",
    "$$\n",
    "plim_{x \\to \\infty} \\hat{\\tau}^{TWFE} = VWATT - \\Delta ATT\n",
    "$$\n",
    " \n",
    "The first term is the variance weighted ATT from multiple DiD comparisons like the ones we saw earlier. This is what we want. However, there is also that extra $\\Delta ATT$ term there. This represents how much the ATT changes over time and it is what bias our estimate. Looking at this term, we can see that we will have downward bias if the magnitude of the effect increases with time (like in our example) or upward bias if the magnitude of the effect decreases with time. \n",
    " \n",
    "In the example above, we saw that the effect from TWFE was smaller than the true ATT. But the situation can be even more extreme. I think it is worth going over one last example to see that this bias can be so strong as to even reverse the signal of the true ATT. Let's consider a very simple process, with only two cohorts. Here, the treatment effect will be negative and decreasing by 0.1 every day. I've also removed all time fixed effects and the trend so we can really see what is going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d0360009",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "date = pd.date_range(\"2021-05-15\", \"2021-07-01\", freq=\"D\")\n",
    "cohorts = pd.to_datetime([\"2021-06-01\", \"2021-06-15\"])\n",
    "units = range(1, 100+1)\n",
    "\n",
    "np.random.seed(1)\n",
    "\n",
    "df_min = pd.DataFrame(dict(\n",
    "    date = np.tile(date, len(units)),\n",
    "    unit = np.repeat(units, len(date)),\n",
    "    cohort = np.repeat(np.random.choice(cohorts, len(units)), len(date)),\n",
    "    unit_fe = np.repeat(np.random.normal(0, 5, size=len(units)), len(date)),\n",
    ")).assign(\n",
    "    trend = 0,\n",
    "    day = lambda d: (d[\"date\"] - d[\"date\"].min()).dt.days,\n",
    "    treat = lambda d: (d[\"date\"] >= d[\"cohort\"]).astype(int),\n",
    ").assign(\n",
    "    y0 = lambda d: 10 - d[\"trend\"] + 0.1*d[\"unit_fe\"]\n",
    ").assign(\n",
    "    y1 = lambda d: d[\"y0\"] - 0.1*(np.maximum(0, (d[\"date\"] - d[\"cohort\"]).dt.days))\n",
    ").assign(\n",
    "    tau = lambda d: d[\"y1\"] - d[\"y0\"],\n",
    "    installs = lambda d: np.where(d[\"treat\"] == 1, d[\"y1\"], d[\"y0\"])\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6a7d8e11",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "[plt.vlines(x=cohort, ymin=7, ymax=11, color=color, ls=\"dashed\") for color, cohort in zip([\"C0\", \"C1\"], cohorts)]\n",
    "sns.lineplot(\n",
    "    data=(df_min\n",
    "          .groupby([\"cohort\", \"date\"])[\"installs\"]\n",
    "          .mean()\n",
    "          .reset_index()),\n",
    "    x=\"date\",\n",
    "    y = \"installs\",\n",
    "    hue=\"cohort\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d0f931",
   "metadata": {},
   "source": [
    "Looking at the plot above, we can clearly see that the ATT is negative right? The correct counterfactual should be a straight line at about 11. However, if we run the TWFE estimator, we get a positive effect!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bf4919aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04999999999998828"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = f\"\"\"installs ~ treat + C(date) + C(unit)\"\"\"\n",
    "\n",
    "twfe_model = smf.ols(formula, data=df_min).fit()\n",
    "\n",
    "twfe_model.params[\"treat\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85d16ab",
   "metadata": {},
   "source": [
    "Once again, to see what is going on, focus your attention on the comparison where the early treated cohort serves as the control for the late treated. Remember that, like DiD, TWFE adjusts the trend from the control group to the level of the treated group, so the counterfactual should reflect that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "deb3ca26",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pred = df_min.assign(**{\"installs_hat_0\": twfe_model.predict(df_min.assign(**{\"treat\":0}))})\n",
    "          \n",
    "plt.figure(figsize=(10,4))\n",
    "[plt.vlines(x=cohort, ymin=7, ymax=11, color=color, ls=\"dashed\") for color, cohort in zip([\"C0\", \"C1\"], cohorts)]\n",
    "sns.lineplot(\n",
    "    data=(df_pred\n",
    "          [(df_pred[\"cohort\"].astype(str) > \"2021-06-01\") & (df_pred[\"date\"].astype(str) >= \"2021-06-15\")]\n",
    "          .groupby([\"cohort\", \"date\"])[\"installs_hat_0\"]\n",
    "          .mean()\n",
    "          .reset_index()),\n",
    "    x=\"date\",\n",
    "    y = \"installs_hat_0\",\n",
    "    alpha=0.7,\n",
    "    ls=\"dotted\",\n",
    "    color=\"C0\",\n",
    "    label=\"counterfactual\",\n",
    ")\n",
    "sns.lineplot(\n",
    "    data=(df_pred\n",
    "          .groupby([\"cohort\", \"date\"])[\"installs\"]\n",
    "          .mean()\n",
    "          .reset_index()),\n",
    "    x=\"date\",\n",
    "    y = \"installs\",\n",
    "    hue=\"cohort\",\n",
    "    legend=None\n",
    ")\n",
    "plt.ylabel(\"Installs\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7dedffc",
   "metadata": {},
   "source": [
    "Notice how the counterfactual level is pushed down from where it should be. The fact that the early treated group effect is decreasing pushed this level down from 10 to about 9.5. Not only that, the counterfactual also adjusts for a downward trend, which should not be there. It is pretty clear from the plot that the right counterfactual should be a straight line at 10, but instead, it is a downward sloping line, since that is what it sees in the early treated group it uses as a control.  \n",
    "\n",
    "The end result is that the counterfactual $Y_0$ is actually below the $Y_1$ outcome, leading to a positive impact estimation. This is pretty awkward. By simply looking at the plot, we can pretty much see where the right counterfactual should be (the straight line at 10). Still, TWFE cannot recover that simply because it uses the early treated as a control for the late treated. \n",
    " \n",
    "#### Event Study Design\n",
    " \n",
    "Just to get it out of the way, I know someone might think we can easily solve this problem by what is called an event study design, where we add one dummy for each period before and after the treatment. In this case, we replace the original TWFE model by\n",
    " \n",
    "$$\n",
    "Y_{i,t} = \\tau^{-K}~ D_{i,t}^{<-K} + \\sum_{k=-K}^{-2} \\tau^{lead} D_{i,t}^{k} +\\sum_{k=0}^{L} \\gamma_k^{lag} D_{i,t}^{k} + \\gamma_k^{L+} D_{i,t}^{>L} \\gamma_i + e_{it}\n",
    "$$\n",
    " \n",
    "where $D^k_{i,t}=1\\{t-\\text{Cohort}_i=k\\}$ is an event study dummy that is 1 if the unit is `k` periods away from the treatment and 0 otherwise. I know this looks messy and complicated, but it is actually very simple once we look at it through some code. All we need to do is create a `relative_days` column which measures how far away is the unit from the period where treatment starts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "86ccad1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>unit</th>\n",
       "      <th>cohort</th>\n",
       "      <th>unit_fe</th>\n",
       "      <th>trend</th>\n",
       "      <th>day</th>\n",
       "      <th>treat</th>\n",
       "      <th>y0</th>\n",
       "      <th>y1</th>\n",
       "      <th>tau</th>\n",
       "      <th>installs</th>\n",
       "      <th>relative_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-15</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>-3.435864</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-16</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>-3.435864</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-17</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>-3.435864</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-18</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>-3.435864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-19</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>-3.435864</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.656414</td>\n",
       "      <td>-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  unit     cohort   unit_fe  trend  day  treat        y0  \\\n",
       "0 2021-05-15     1 2021-06-15 -3.435864      0    0      0  9.656414   \n",
       "1 2021-05-16     1 2021-06-15 -3.435864      0    1      0  9.656414   \n",
       "2 2021-05-17     1 2021-06-15 -3.435864      0    2      0  9.656414   \n",
       "3 2021-05-18     1 2021-06-15 -3.435864      0    3      0  9.656414   \n",
       "4 2021-05-19     1 2021-06-15 -3.435864      0    4      0  9.656414   \n",
       "\n",
       "         y1  tau  installs  relative_days  \n",
       "0  9.656414  0.0  9.656414            -31  \n",
       "1  9.656414  0.0  9.656414            -30  \n",
       "2  9.656414  0.0  9.656414            -29  \n",
       "3  9.656414  0.0  9.656414            -28  \n",
       "4  9.656414  0.0  9.656414            -27  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_min_rel = (df_min\n",
    "              .assign(relative_days = (df_min[\"date\"] - df_min[\"cohort\"]).dt.days))\n",
    "\n",
    "df_min_rel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364c7f62",
   "metadata": {},
   "source": [
    "Then, we can pass that column as a category so our model will estimate the expected number of installs for each period relative to the treatment. We can then define the effect as the extra expected number of installs compared to relative day -1, which is the last day prior to the treatment.\n",
    " \n",
    "We might think that this formulation would capture the time heterogeneity in the ATT and solve all our issues. Unfortunately, that is not the case. If we try it out and plot the counterfactuals, we can see that they are far from where they should intuitively be (the horizontal line at 11)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "050fb702",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the intercept, otherwise effects will be relative to relative day -30\n",
    "formula = f\"installs ~ -1 + C(relative_days) + C(date) + C(unit)\"\n",
    "\n",
    "twfe_model = smf.ols(formula, data=df_min_rel).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2fb993bc",
   "metadata": {
    "scrolled": true,
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAEJCAYAAAA6kzulAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABTsElEQVR4nO3dd3gc1bn48e+Z3VUvq2q5W5Yl99673C1hwDSTUEIPhEACKdwb8guX3EuIE4cYCBhDbGoScCimWu623AsYXLCxJNvgbqtXS5Y05/fHGmEHl5W80mx5P8/DA7Nl5t0zw+rdM+e8R2mtNUIIIYQQwjKG1QEIIYQQQgQ6SciEEEIIISwmCZkQQgghhMUkIRNCCCGEsJgkZEIIIYQQFpOETAghhBDCYnarA2iKo0ePNuv+4+PjKSgoaNZj+BNpL98i58t90laNI+3lPmkr79AS56FNmzZuvU56yIQQQgghLCYJmRBCCCGExSQhE0IIIYSwmE+OIRNCCCH8ndaa6upqTNNEKWV1OH7pxIkT1NTUXPZ+tNYYhkFISEiTz5UkZEIIIYQXqq6uxuFwYLfLn+rmYrfbsdlsHtlXXV0d1dXVhIaGNun9LXLLcs6cOdx999388pe/bHhs48aN/OIXv+DGG29k3759LRGGEEII4TNM05RkzIfY7XZM02zy+1skIUtPT+fRRx8957H27dvzq1/9iu7du7dECEIIIYRPkduUvudyzlmLpN49evTg5MmT5zzWrl27lji0EEIIIYTX84m+0OXLl7N8+XIAZs6cSXx8fLMez263N/sx/Im0l+8o+n8/pVgp4v/vOatD8QlybbvvgXd2oNQx/nZdb6tD8QnuXFsnTpzw+1uWL774IrfeeithYWGNel9ubi733nsvSinmz59Pp06d3H7v+vXrCQoKYvDgwQBNauPk5GQOHDjwvceDg4Ob/J3hE2d64sSJTJw4sWG7uavqSgXlxpH28h31tbU4HA45X26Sa9t9tXJtNYo711ZNTY3HBpx7q5deeolrrrmGoKAgt99TX1/PJ598QmZmJg899BDgGlDvrnXr1hEeHk7//v2x2+2Neu/Zzve+mpqa751XqdQvhBBCiMv29ttvN3SMPPjggxw6dIgbbriBiRMnMmPGDI4cOQLAQw89xMcff9zwvtTUVAA2bNjA9ddfzz333MOYMWN44IEH0Fozf/58Tpw4wQ033MD1118PQHZ2NldeeSVTpkzhxz/+MZWVlQAMHTqUP/zhD0yZMoUPP/yQefPm8cYbbzS8784772Tq1KmMGzeOf/zjHw0xrFq1iilTpjTEeujQId544w3+/ve/M2nSJDZt2nTBuCsrK5kxYwZTpkxhwoQJLFmypBlb2Ud6yIQQnmHc998442IpOt20X4RCXMh/jW5LbFwcdZWlVofit8z3XkN174fq3hddX4/+4B+oHv1R3fqga2vRH/0L1WsQKq0nuqYa/ckCVJ8hqC7d0aeq0Flvo/oPRyWnoSsrUOERlzzm3r17eeaZZ/jwww+JjY2luLiYhx56iBtuuIEZM2bw1ltv8bvf/Y6XX375ovvZtWsXK1euJCkpiauvvpqtW7dy11138dJLL/H2228TGxtLUVERzzzzDAsWLCAsLIznn3+el156iYcffhiAmJiYhqRo//79hIeHc9999wHw1FNPERMTw6lTp7jiiivIzMxEa82vf/1r3nvvPTp06EBxcTExMTHceuutDe+12+3nJHBnCw4OZv78+URGRlJUVMSVV17J5MmTm22yRYskZE8//TS7d++mvLyc++67jxkzZhAREcHLL79MWVkZM2fOpFOnTvz2t79tiXCECFgqMgojyglyW0l4WFSIHWeog4JKqyMRnrR+/XqmTZtGbGws4EqKPvvsM+bNmwfAddddxxNPPHHJ/fTr16/h1l3Pnj05dOgQQ4YMOec1n332GTk5OVx99dWA6zb4wIEDG56/6qqrLrj/l19+maysLACOHj3KgQMHKCwsZNiwYXTo0KEh9sbQWjNz5kw2b96MUorjx4+Tn59PYmJio/bjrhZJyL69x/uf/vNkCCGal7l+BaciI6DPUKtDEX5mxb4SIk7WMzTRv8c8Wcm49raG/1Y2G+rsbYfj3O3gkHO3Q8PO3Xajd6yxzq7DZZomtbW1Dc+dPUbMZrOdd/yV1poxY8YwZ86c8+7/QgP/N2zYwNq1a/noo48IDQ3l+uuvb1T1/QvF/d5771FYWEhWVhYOh4OhQ4d6pKr/hcgYMiECiN6wglMrF1kdhvBDK/eXkrX7hNVhCA8bOXIkH3/8MUVFRQAUFxczaNAgPvjgA8CVtAwd6vqB165dO3bu3AnA0qVLz0nILiQiIoKKigoABg4cyNatWxtmL1ZVVblVOL68vJzo6GhCQ0PJy8tj27ZtDfvbtGkTBw8ebIgdIDw8vOGYF4u7vLyc+Ph4HA4H69ev5/Dhw5eM5XLIGDIhhBBCnFfXrl352c9+xvXXX49hGPTq1YsnnniChx9+mLlz5xIbG8vs2bMBuPnmm7njjjuYOHEi48aNc6uUxc0338zNN99Mq1ateOedd5g9ezY//elPOX36NACPPPIIKSkpF91Heno6b7zxBmPHjiUlJYUBAwYAEBcXx5///GfuvvtuTNMkPj6et956i0mTJnHvvfeyZMkS/vjHP14w7muvvZbbbruNCRMm0KdPH7p06XI5TXlJSmutm/UIzeDo0aPNun+Z6t440l6+o37WozgcDsyHfm91KD5Brm33/XbZNzgcDh5Pd2+Kf6Bz59qqqqpqdH0u0TiXU/bifM53zqTshRBCCCGEj5CETAghhBDCYjKGTIgAYvzsf4iJj6OwvOLSLxaiER4b1564uDgqSoutDsVv+OCIooB3OedMesiECCAqOBgVHGJ1GMIPBdsNQhxS8sKTDMPw6Pgm0bzq6uowjKanVdJDJkQAMVctoioiHAaPtToU4WcW5RQTcaSWMW0dVofiN0JCQqiurqampqbZqsMHuuDgYI/UFtNaYxgGISFN/8ErCZkQAUR/uo5qh0MSMuFx678pw+E4xZi2MsvSU5RShIaGWh2GX/OmmdRyy1IIIYTP01qjq2TdJuG7JCETQgjh+z7fiF7yXsOmueoT9GfrG7b10YNomXAgvJgkZEIIIXxfaDicPWGluhp9pto7gF78LvrTdQ3b5sI30F9s+u75vD3o4sIWCdVf6bISzI8XoCvLXdtF+eg929FnliLSdXXoM2tGiu+ThEwIIYTPU937YmTe0LBtZFyHMXzcd89nzkD1HwacKU0QFAx214LXur4ec/G76Jxd322/+iz6y88btvXOTyVhu5TiAvSBHKipdm1/nYe5/EMw613bO7ZgznkSXetKlHXubsyl76PPPK8LT6IP7g/Ych8yqF+IAGL79ZPEetEgVuE//jCpo2UDpHVRPjjjUBcpOaCS2n7330qhrpjx3ZOGgXHTfRDkStCoq4U2HSA8wrVdWY65OgtjwjSIiUOXlaDf+jtq/BWoLj3Qp6pg9+eQ0h3ljD3TC6RRRmCVAVEdu2B78HffPdBnEEbnrq7kF6B1e9SQMSjHmXauLIP8Yw3tpL/cBnt2YNz7CADmmiVwcB/GLfe7nt/3FZyqRPUa6NrW2q9mn0pCJoQQwmfpulr0269Atz6osVObtA+lFMTGf7cdHIKaPP27F0RGY9z5ENjPlPQwDOjaG6JjXdslhZgbVmIkJIEzFo4fxnzvdYzpt6DadUIXFaC/2oHqPQgVGeWK2QOlFrydsjtc7fHtduv2qNbtv9vuNwzVb9h32wNHQtc+320ntUV/m7yBqwezuOC7hGzxu+iaaozpt7i29+8FuwPVoXOzfabmJAmZEAHEXLKQyvBwGDXZ6lCEn1m4u5Dw8Gomd2zhwsPKQE24EqJjmu8QSkF45HfbEVHnJH+qdXuM+/4L1JkeurAIV3LxbcJWXID+fCOqW2/X9v69lKz6BH3DnajYBPSRb9C5u1HDxqJCwtDVVVBXB+GRPtUDpHdsRR85iJFxXZPer8Ijz23ntF6c/enV1Ovg9HeJrGqXDLVnjRPcsgbCwhsSMvPDf4EzDmPMFNfz3+RBRDQqLqFJ8TU3GUMmRADRO7ZS8+n6S79QiEb69EgFGw4Utfhxlc2G6tIdlZDU4sc+Jw5HEMru6uNQzliM4eNQkVGu7ZRuGD/5DcSc6YWLb0Xo+EyIdLq2S4sgZxd8e4vzyy8wX3mmIdnQOV+6xlrVnxlrVVbiGjDvbWOt6mrhdHWz7V4pdc5KI6r3QNSA4d9tX/Mj1Php370hJh4V5WzY1Es/QG/f/N32to3NFmtTSA+ZEEIIn6Qry2H/XkjrjQoOtjqcizp7fJuKTSAkrTsVZ8bbqR79UT36f/fiTqkYIaGob8deVZVDwQmU7cxYq883wd6dGD/+tWt72wZ0wQmMyde4tk8eA61RrVq2SK8aMAI1YESLHvOc4wcHw1nXgTH63DsB6rrb4Ns2NOu/GyPoJSQhE0II4ZsO5LgG27dPPucPsa9TcQlw1m2174216j0QOqQ0bOv6etctzm+3t6yBsmLUTfcBYK5eBPX1GBOudD1/cD84HOeM5woE6uxxgobNNQ7Qi0hCJoQQwjf1HIDRpiPKGWd1JC1KxSZA7HcJmzF49LnPj5oIZ08aCA79rvQEoDeuhNAw1FU3eTQuc/mHEB55TrkR4T5JyIQIJEFB3005F8KDgmwGQfaWLfPwn7Mjhct/Jqj/mSCpzBlQX4fHKQW+MwfB60hCJkQAsf38cWKkDploBv8zvn2L1iEzN2eD1hjD0lvkeP5ERUahqyrR5WUNEw884dtboqJpZJalEEII31NeAmUlVkfhs/SS985Z+1NYT3rIhAgg5sdvUREWDuPll6zwrAU7CwgLq+LKlLAWOZ4x8WrvK/vgQ1yzIT3XfrquFv3ua6h+Q1FeNljeV0gPmRABRO/Zwekdn1odhvBDO45X8tmhkhY51reLVftS0VRvozqmoDp28dwO603XAu826edpKknIhBBC+AxdVYE5/yn0VzusDsWn6eoqdMEJj+1PBQdjXPVDVJfuHttnoJGETAghhO/Q2lVENbG11ZH4NL3jU8w3X2qo/i+sJ32LQgghfIYKj0SdWZtQNJ1K6X5OodTLpY8eRGcvRk26GhXfymP7DSSSkAkRSCIiMYKCMa2OQ/idyGAbQUGOZj2GLi+F0zWouMRmPU4g+M/VAC6bYYPIaLA37zXgzyQhEyKA2H7yG5xSh0w0g/8e067Z65DpLzajd3yKcfcvzllkWjSerj0NxYUQHeuRdUBVUlvUtBs9EFngkjFkQgghfIIaOBIj8wZJxjzh5HHMBfPgxBGrIxFnSEImRAAx33uN8jdesDoM4Yde//wkL6z/ulmPocLCUcmpzXqMgBEXj3HFDPDQeC+9bSPmP+dKbbjLIAmZEAFE79tL7d5dVoch/NDeglN8eays2favt21EH9zfbPsPNCokDNW5Kyos3DM7DI+EhCSpDXcZJCETQgjh1XR9PXr7FvTXuVaH4je01ujjR9AeWn5Kde2FMXm6R/YVqGRQ/3+orjMpqjxNyak6q0PxGYa0l8+oN0KwKwdazpdb5Np2X52psTfT7Spls8GPHoC62mbZf6Ay33kFNXAkavg4q0MRSEL2PUvzSpj/WY7VYQjRPJJmuP79Xp61cQi/5DBqeGtnAZNSookL82z5A2Wzgc3m0X0GMqUUxpU/hOgYj+zP/OTfYHdgTLnGI/sLRC2SkM2ZM4dt27YRHR3NU089BUBFRQWzZ88mPz+fhIQEHn74YSIiIloinIvqnRjKL9M7U1FZYXUoPiMiPELay0fo9Stw2O3UD023OhSfEBERQUWFXNvuWLU3H44f5s0dmgU7CxjWJoyp6hi9e3XGiEtAa92k8UW6KB+99H3U+Gkoqc7vUapjiud2ltgGZZNRUJejRRKy9PR0pk6dyvPPP9/w2Pvvv0/v3r2ZPn0677//Pu+//z633HJLS4RzUR23LaXDgvlWhyFEs3F07UVdoRM1aCTKEWR1OF6tuetq+ZOpHYKJPFBJTlgkSwodrMgrZkNtNO0KC5naw0Z65CnCF72JyrgO1S4ZXVkO3+yDTqkXH1heUw2GARGRLfdhAoTOPw6miWrV5rL3ZQwe5YGIAluLJGQ9evTg5MmT5zy2detWHn/8cQDGjh3L448/7hUJmUruStgP7qKqssrqUHxGWHiYtJevqKvF3L4F/fJs9L/noUZOQo2dikpIsjoy4eNUSBghoyfRpqCAOzrCTX3iWXegmMV5Zcz77CRv2GB0/Bgy68NIATh2GHPFRxg/uAfCwtFf56LXr0BdMQPljHUNNi8uhLYdMGbcZfGn80963TKor0Ndf4fVoQgsHENWWlpKTIzr3rXT6aS0tPSCr12+fDnLly8HYObMmcTHe279re+JH4V9ZDoRdTKQ1112u13ay0eUz38aBg4n6N5fcWrxe9Qs+wC9dCFB/YcRNvUaggYMd43VEYDr2m7W7xs/8nT2fgyjnJ+NTm547MakRG4cDl+dqGDhzmMs22uwfF0xPfLquKZXF9If+C2hsbEom526ylKq27QlrG07jNAwqvfv4dSyD4l+6H8wwq0fzuJp3nBt1V05A2UY2C4zDq01ZU//nuDh4wgZNtZD0bUMbzgP3/KKQf1KqYuOLZg4cSITJ05s2G7uWwhym6JxpL18R33ObhwOBzVX3Qx3/gLjmtvQa5dwes1STj/5CMQlosZMQY2ahIpyWh2u5eTadt+eo8U4HI7ztle8De7pF8MPu0ex6kApWbkl/GH5Pp4NtjGhcwFTU520joyGcVdSXVkFlVXoNh1h2g8prDqFOlVtwSdqXl5xbdnPLJl0mXHo+np0hy6ctgVRYfVnaqSWOA9t2rh3S9iyhCw6Opri4mJiYmIoLi4mKirKqlCECFgqJg511U3ozBmwfQvm6kXohW+gP3wTNXAEamwGpPaQYo/CIyKCbVzZLZZpXWPYcaKKrJwSPvyqiPf3FDGgdTgZaU4GtonAZihUSBi07WB1yH5NlxRBcQEqOe2y9qNsNtTYqR6KKnBZlpANGjSI7Oxspk+fTnZ2NoMHD7YqFCECnrLbYeAIbANHoI8dQmcvRm9Yid6yBtp2RKVnoIalu/5ICnGZlFL0TQqnb1I4hVW1LMsrZUleCX/IPkJCmJ0pqU4mpThxhnrFTRy/pXN2oTdnY9z/qAxV8AJKt8DCU08//TS7d++mvLyc6OhoZsyYweDBg5k9ezYFBQWNLntx9OjRZo3XK7qSfYi0l++on/UoDocD86HfX/K1uqYavTkbnZ0FB/dDcChq+DhUegaqbcfmD9YLyLXtvt8u+waHw8Hj6U2bsVdnarYcLicrt4Qdx6uwGzC8fSQZaTH0SAj1u15ab7i2dHkZVFe5hioYTS9ZoY8dRn/wT9S0G1HtOnkuwBYQcLcsH3roofM+/thjj7XE4YUQZ6hWbbCHhHDandcGh6DGTEGPngwHctCrF6HXLUOvXuS6jZmeiRowHGX3bAFQ4ZvaRAUREhLS5PfbDcWIDlGM6BDF4bIaFueWsHJfKWu/KadjdDBT05ykJ0cR5pCeHE9RkVEQ6YHhQqGh0LM/RMjQo8vRIj1kniY9ZN5F2su3XM750uVl6A3L0dmLIf84REajRk92TQSIS/RwpNaTa7txPN1eNXUma74uIyu3mH1FNYTYDcYlRzE11UmnmKYnf97AG64tXVUJR7+BNh09t8i4jwm4HjIhhH9QkVGoKdeiJ02H3Z9jrs5CZ72LznoX+gzCSM+AHv0v6/aHEN8KthtM6uJkYko0uYXVZOWWsGK/a5Zmj4RQMtJiGN4+AodUiG+akkLMrHcxrr4ZOnRu8m6augqDOJf0kJ2HN/xy8SXSXr7DfP05QkJCOD3jbo/tUxeeRK9Zgl67FMpLISHJVWx2xETXLREfJte2+57ffIyQkBDu6uuZtREvpKymnpX7S8jKKeF4RS3RwTYmdXEypYuTxAjfuX3uDdeWrj0NpcUQHXNZq3boz9ZjblmDcc+vfG4Ig/SQCSEsoU8cpc7h4UWf4xJR19yKvvIH6G0bXWPN3nkV/f4/UYNHuUpndO4qv6D93NGy0zhOaaB5E7KoYBvTu8dxVbdYth+vIiunmPd2F/Lul4UMahtORmoM/duEY8j1dknKEQTxrS5/R63aoPoMAZukFJdDWk8I4RHK7kANGQNDxqCPfINenYXetAq9cRV06IxKz0QNGYMK9u2xP8I7GErRv3U4/VuHk19Zy9K8EpbmlbD1yGGSIhxMOXOrMypE/sxdiDZNyNsNMfGXtXyaapeMapd86ReKi5Ib70IIj1NtO2LcfB/GrFdQN/8E6uvRrz+H+es7MN/6O/rYYatDFH4kIdzBzX0TmDe9C78a2Ya4MDuvfZHPnQv3MXv9Ub7KP4UPjs5pfkphLn0fnbfnsnaj6+ulfT1AfjoIIZqNCglDpWegx06FfXvQq7JcPWcrPoKuvTHGZULfoa7CtEJcJodNMbpTFKM7RXGwpIas3GJW7S9j9ddlJMcEk5kWw5hOUYTYpS8CXAV6jZt/Apc5w1J/8m+orkLJIvCXRb4FhQggqn0yjtBQalr6uEpBlx6oLj3QN96FXu8qnWHO/RNEx6LGTEaNnoKKiWvhyISnJMeEEBIaanUYDTo4g7l3cBK39ktwlc7IKeH5zcd5ZdtJxnWOJiPVSfvoYKvDtJwn/p9TXXtBXZ0HoglsMsvyPLxh9osvkfbyLd5yvrRZDzu3YWZnwa7PQCnoNxQjPRO69fGKSQDe0la+wpvbS2vNV/mnyMotYf3BcupMTa9WYWSmOhnaPhK70bLXm7e0lf4mD0zzstez9FUyy1IIEfCUYYO+g7H1HYzOP+5aP3P9MsxtG6FVW1T6VNTwCahw95ZUE+JilFJ0Twyje2IYdw6sY8W+UhbnlvDndUeJCbUzuUs0k7s4iQ/zrbINl0tv2wT1tZeVkOnTNeAI8oofUb5MesjOw1t+ufgKaS/fYc57iuDgYGpvfcDqUM5L155Gf7YevToL9n0FQUGowWNQ4zJRHbu0eDxybbvvr+uPEhwczE8H+c5t53pT8/mxShblFLPtaCVKwZB2EWSkxtAnKaxZS2d4y7WlKyvAZqBCwpq8j/q5M1E9B2CMnuzByFqG9JAJISyhiwup93AdMk9SjiDUsHEwbBz64H5XTbPN2ej1yyE5DTU2w1XbLEjG/nibwqpaHLVWR9E4NkMxqG0Eg9pGcKLiNItzS1i+r5RNhypoE+lgamoMEzpHExHsv+tneqIH2hiWDvFNL5shXCQhE0J4JdWhM+pHD6CvvwO9cRU6Owv96jPof89HjZroWg0g0b1fnkJcSquIIG7rn8hNfeJZf7CcrJwSXt52kn9sz2d0xygy0pykxnnPpAVP0cePwIkjqL5DmrwP1W+YByMKXJKQCSG8mgoLR02Yhh5/BeTsQq9ahF7xEXrp+9Cjv2v9zD6DUTb/7cUQLcdhM0hPjiY9OZoDxdVk5ZSQ/XUpK/aXkhoXwtRUJ6M7RhHsJ6Uz9KH96E2rMXoNbNL/Q7q+Hk7XQHCwa1yoaDJJyIQQPkEpBV17o7r2RpcUodctRWcvwZzzpKvS+JgpqNGTUdHNu3SPCBzJMSHcPzSJ2/onsPpAGVm5xfxtk6t0xoTO0UxNjaFNVNPXgPQGqu8QVJ/BYDQxwSzKx3zr7xiZN0BKN88GF2AkIRMigKiUrjhCw1q8DpmnKWcsatoP0Bk3wPYtmKsXoT/4J/rjt1D9h6PSMyGtp8z6akFd40MJDWv6wHBvFh5k44quMWSmOfny5Cmycov5eG8xH3xVTL+kMDLSYhjcNgJbC5fO8ITLHo8ZHoExZgokeGBNzAAnsyzPw1tmv/gKaS/f4q/nSx8/4iqdsWE5VFVC6/ao9AzUsHGoJlYi99e2ai6B1F7Fp+pYllfCkrwSCqrqiAuzM6WLk0ldnMSGXrqvw1vaSpeXQu6XkNoLFRlldTgtTmZZCiGEh6mktqgb70JPvwX96VrXWLM3X0K/9zpqaDoqPQPVXhZAFp4RE2pnRu94rusZx6dHKliUW8K/dhSwYGcBw9pHkpHmpFdimPf30paXYq5fgRGfBE1IyHRNDdTVQli4939WLyc9ZOfhLb9cfIW0l++of+GPBAcFU3fXL6wOpUXoA7no7EXoLWuh9jSkdEOlZ6AGjkQ5Lj32R65t981cc5igoGB+MSzB6lAsc7TsNEvySli+r4SK0ybtooLISHMyLjma8KBzB7x7y7Wl6+vBrAe7o0kJld6+BXPNEox7fnlZtcysIj1kQghrVJRjOqqtjqLFqORUVPLP0Tfcid6w0rWw+fzZ6AXzUaMmuSYCJEj9JE8or6nHYfpYITIPaxMVxB0DXKUz1n1TRlZuCX//9CSvf57P2OQoMlJj6BwbYnWY51A2G1zODOV2nVwznaU24GWThEwI4fdUeCRq0tXoCVfCV9sxV2ehlyxEL3kPeg10/UHpNUCm7QuPCLYbTEhxMiHFSW7hKRbnlrD6QBlL80rpGh9KRqqTq5yxVocJgDZN9GcbUK3bodp1avT7VVwixCV6PrAAJAmZECJgKMOAHv2x9eiPLipAr12KXrsE82//B3GJrmKzIyeiopxWhyr8RGpcKKlxodzRP5GVB0rJyinm6Y3HeOXzfCZ0jmJKFydJkRaWzlAKvSUbBo5oUkKmK8pcu4kIvAkBniYJmRAiIKnYeNTVN6GvmAFfbHL1mr33OvrDf6EGjESNy0DHjbY6TOEnIoJtXNUtlmldY9hxvIoV31Tx/p5CFu4uYkCbcDJSYxjQJrzFS2copTDufQRlb9qSanrtUijKR938Ew9HFngkIRMigKjufQgKCydwRpFdmrLbYdAobINGoY8dOlM6YwV6SzZFnbpgjpyEGjbWJwcst6Q+SeGE+WkdMk8ylKJf63Am9u7IVwePsTSvhKW5JTyRfZjEcDtTusQwsUs0zpCW+/Pc1GQMcBWVrZFvFE+QWZbn4S2zX3yFtJdvkfN1abqmGr05G9u6pdQdyIWQUNTwcaixmai2HawOz2vJteW+s9uqztRsPuxaP3PniSrsBozoEEVGqpPuCaHNXk5Cf/k5aI3qNaBZj+ONZJalEEJ4MRUcghozhdhrbqJgy3rX7My1S9GrFrlWAEjPRPUfdlk9C0J8y24oRnaIYmSHKA6V1rA4t4SV+0tZ83UZHZ3BZKQ6GZscRZijeSad6JwvAZqUkOmifNcPlrAIT4cVcKSH7DzkV17jSHv5jvpnHifIEUT9/Y9aHYpPOPva1uVl6A3L0auzoOAERDlRoya7SmfEBW7trW/9fuUhgoKC+M0oWULHHZf63qyuM1nzdRmLcoo5UFxDqN0gPTmKjLQYOjo9W2JCa93kXjhz3lOQ0g1j3BUejamlSA+ZEMIap0/jg7/BvIKKjEJNuRY9aTp8+TlmdhY662101jvQdzDG2Azo0c81kzMAna430XX1VofhN0LsBpO7OJmUEk1OYTVZOcUs31dKVm4JPRJCyUiLYXj7SBy2y7+deTm3RNWEqyC8aUuTiXNJQiaEEI2gDAN6D8TWeyC64AR6zRL0umWYX2yGhCRUegZqxAQpAyA8QilF1/hQusaHcueAOlbsL2VxbglPrT9KdIiNSSlOpnRxkhjR9Nvn+pt96IP7MEZPbnx8yalNPq44lyRkQgjRRCq+FeraH6Gv/CF62wbXWLO3X0Ev/Adq8GjUuEzolCpr/AmPiAqxc02POK7uHssXxyrJyi3hvd2FvLe7kIFtIshMc9KvdThGY6+3/GOwZzt6xARX5X436bo61+17ZywqJLSRn0b8pyYnZCdOnEApRWKiVOgVQgQ25XCgho6FoWPRh79GZ2ehN65Gb1wJHVJQ6RmoIWNRwbK8jLh8hlIMaBPBgDYR5FfWsiS3hKX7Sti6qoKkCAdTUp1M7BxNlJulM9SgUahBoxofSHkp5tsvY0yeDl17N/794hxuD+p/+umnycjIoGvXrqxatYp58+ZhGAZ33HEH48ePb+44zyGD+r2LtJfvMJcsJDw8nFOjGn9rIhBdzrWtq6vQm1a7JgEc+QZCw1EjxqPSM1BJ7TwbqBdYuLuQ8PBwJnf0rrUavZWnvzdr6zUbD5WzOLeYL0+ewmEoRnWMJCMthrS4kGbppdWna+DIQUho5bO36L1pUL/bCdndd9/N3Llzsdvt/PKXv+See+4hPDycWbNm8eyzz15WsI0lCZl3kfbyLXK+3OeJttJaQ+5uV6/ZZxugvg669cFIz4S+Q1yFaf2EXFvua862+qakhqycYlYfKONUnUnnmGAy0mIY0ymKEPv3J53oogL09i2uUi5essZmS/GmhMztb4K6ujrsdjtFRUVUVFTQrVs3AEpLS5sWoRBCBACllKt2WVpP9I3F6LXL0GuWYM6d6Rp7M3oKasxklDPO6lCFn+joDOa+IUn8qH8C2QfKyMot4fnNx3l120nGd45maqqTdtFn3T6vOQX79kBaL2hEQqYrK6Cs2DWZRWryXTa3E7JOnTqxcOFC8vPzGTDAVTyuqKiI0FAZyCeEr6if9ShFDgc89HurQwlIKioGdcUMdMZ1sPMzzNWL0B+/hf5kAfQbhpGeAd36+OQkgN8u+waH4yiPp7vXGyCaX5jDRkZaDFNTnezJP0VWbglZucV8tLeYPq3CmJrmZGi7SOyt26Pu/mXjD/B1LubKjzFu/zlESkJ2udxOyO677z4WLFiAzWbj1ltvBSAnJ4dRo5owEPAsixYtYsWKFWitmTBhAldc4ZvF5YQQwl3KsEHfIdj6DkGfPIZesxi9bjnmtg2Q1BY1NsM13kyqnwsPUErRIzGMHolh3DUwkeV5pSzJK+bPa48SE2pnSpdoJndxEhfWyKSqUxeMq26CMKlD5gluJ2RJSUn8/Oc/P+exYcOGMWzYsCYf/ODBg6xYsYInn3wSu93Ok08+ycCBA0lKSmryPoUQwpeoxNao6+9AX30z+tP16NWL0AvmoRe+7pqZmZ6J6phidZjCTzhD7FzfK45resSy7WglWbnFLNhZyL93FjAkVpHRrz19k8Lc6qVV4ZEQHtkCUQeGiyZkK1eudGsnTZ1leeTIEbp06ULwmang3bt3Z/PmzVx99dVN2p8QQvgq5QhCDR8Hw8ehD+5z1TTbnI1etwyS01DpmajBo1COIKtDFX7AZigGt4tgcLsIjpXXsGThSpaXdmTTykO0iQwiI83J+ORoIoIvXJdMF5yA2tOo1u1bMHL/ddGEbO3atW7tpKkJWfv27XnrrbcoLy8nKCiIzz//nJSU7/8SXL58OcuXLwdg5syZxMfHN+l47rLb7c1+DH8i7eU7ihwOlFJyvtxk2bUdHw8DhmJWllO9Kouqxe9R/8rT8PbLhEyYRuiU6dhbe1fpDIfjqFxbjeBN35vx8dD7F7fyYJ3J6rwC3ttxjPmfneQf2wuYlJbA9D5JdG/1/Z6wyg3LqTu4n+gHfHdtXG86D5YvLr5y5UqWLFlCSEgI7dq1w+FwcPvtt1/0PVL2wrtIe/kOc9UiIiLCqRo81upQfIK3XNtaa9i7E3P1Ivh8E5gm9OzvKp3RZ5BrTJrFFuUUExEewZi2MrjbHd5ybV3I/qJqFueWsPpAKTX1mtS4EDJSnYzqGEXwmdIZuqQIqqt8uq6eN5W9uGhCZpqmWzsxPLSY7r/+9S/i4uKYMmXKRV8nCZl3kfbyLXK+3OeNbaVLCs+UzlgMJUUQm4AaMwU1ehIqKsbS2LyxvbyVt7WVuTkblMIYMuacxytP17PqQClZOSUcLjtNRJDBhM7RTE2NoU2U798+96aE7KK3LH/4wx+6tZMFCxa49brzKS0tJTo6moKCArZs2cIf/vCHJu9LCHFxuqYGXVNtdRjiMihnHOrKH6AzrocdWzBXZ6Hf/wf6o7dQA4aj0jMgtWeLl86oqTOprq1v0WMKDyopgvNcM+FBNqZ1jeWKtBh2nawiK6eEj/cW88FXxfRzKjI6hTG4eztshu+VavE2F+0hy8/Pd2snCQkJTQ7gscceo7y8HLvdzo9+9CN69770eljSQ+ZdpL18R/2sR3E4HJhSh8wtvnJt6+NHXCsBbFgBVZXQpgMqPRM1LB0VGtYiMbjqkDmkDpmbfOXaOp+iU3UsyythyfbDFKoQ4sPsTOniZFIXJzGhvrXyhM/0kF1OouWu//3f/232YwghhD9TSW1RN96Nnn4reusa1wzNf81Fv/saathYVHoGql2y1WEKPxEbaufG3vFcl2SytbCexUdq+eeOAt7aWcCw9pFkpsXQMzHUJwscW6lRqeynn37K7t27KSsrO+fxBx54wKNBCSGEaDwVHIwaNQlGTUIfyHXVNNuwEp29GLp0R6VnogaMQDlk4L04l967C523G+OKGW6/x56QyPAEGN4NjpadZnFuMcv3l7L+YDnto4PISI0hPTmK8CDrJ534ArdH47/99tu89NJLmKbJpk2biIiIYPv27YSFtUx3uBBCCPep5FSMO36OMesV1A13QlkJet5TmP91J+Z7r7lqSAnxrdPVUFGGdnMyn66rQ+d8iS4rBqBNVBB3DmzFK9d04cFhSYTYDV769AR3LsxjzubjHCiWsauX4nYP2apVq/h//+//0aFDB1avXs3tt9/OqFGjePfdd5szPiGEEJdBhUeiJk9HT7wK9mx3TQJYvBC9+D3oNRBjXCb07O8VpTOEdVTvQajeg9x/w6kqzCXvua6fXgMbHg62G0xMcTIxxUlu4SkW55aw6kApS/JK6BYfSkaak5EdInHYPFOdwZ+4nZBVVlbSoUMH15vsdurq6ujSpQu7d+9utuCEEJ6lRkwgNDKCSqsDES1OGQb07I+tZ390UT56zRL02qWYz/4vxCW61s8cNREVGd2k/Y/vHE1EpCyjEzDCwjFuvu+i61imxoWSGhfK7f0TWbm/lMW5xcze4Co6OzElmqmpTlpF+H7pDE9p1FqWhw4don379rRv356lS5cSERFBRIQsfiuErzBGTiA0Pp5KH53dJTxDxSagpt+CnnYj+vPNrrFm772G/vCfqIEjUemZkNKtUYOyJ6Q4fXrmYKDT+cfRG1aiRk1CxV16Qp+y2SDWvYl/kcE2ru4ey5XdYthxvIrFucW8v6eIhbuLGNAmnMy0GPq3Dg/40hluJ2Q33ngj5eXlANx8880888wzVFdXc9dddzVbcEIIz9LlZZhBvjUtXTQfZXegBo+CwaPQRw66SmdsWoXenA3tklHpGaihY1EhoZfcV1l1HfZTtc0ftGgeWrvGkdW7dw51WQkcPQidUt26PgAMpejXOpx+rcMpqKplaV4JS/NK+b/Vh0kMdzAl1cmklGiiQwLzO8rypZOaQuqQeRdpL98hdcgaJxCvbV19Cr0lG70qCw4fgJBQ1PDxqPQMVJsOF3yf1CFrHF+/tvTeXZhLF2Lc/BNUbNPXgqwzNZsPl5OVU8LOE1XYDcXIDpFkpDnpFt/8pTN8pg7Z2e644w5eeeWV7z1+9913M2/ePPcjE0II4bVUSChqzFT06Cmw7ytXr9naJehVn0BaL1R6Jqr/UJRdSmcEtM5pGLfeD5HOy9qNKwGLYmSHKA6V1rA4t4SV+0vJ/rqMTs5gMtKcjO0UTajD/ycBuJ2Q1dd/f0mMuro6t9e7FEII4TuUUq7aZV26o2fchV6/HJ29GP3Sn9HRMa6xRmOmoNwcRyS8m66rQ3/0JqpbH1T3vpd8vXIEgTPOozG0jw7mnkGtuLVfAmu+LmNRTjEvbDnBq9vyGdc5iozUGDo4gz16TG9yyYTsscceQylFbW0t//M//3POc4WFhaSlpTVbcEIIIaynIqNRU69DT54OX37uKp2x6G30oneg7xCM9AyUdlodprgcNhs0ooNFHzsEpcWobn08HkqI3WByF9d4spzCahbtLWZpXimLckromRhKRmoMw9pH4rD51ySASyZk48ePByAvL49x48Y1PK6UIjo6ml69ejVfdEIIIbyGMmzQexC23oPQBSfQaxaj1y3H/GITP4tI4LO0sejB16DCpfyFr1FKoa67ze3X6692Qt6eZknIzo6pa3woXeNDuWtgHcv3l7I4t4S/rD+KM8TGpBQnU1KdJIT7x+1ztwf1HzlyhLZt2zZ3PG6RQf3eRdrLd5hb1xIVGUlFt35Wh+IT5Nq+NF1bi962gdLFHxF1OAccQajBo1HpmajkVKvD81q+fm3p0zVQU4OKjGrR45pa8/nRSrJyS/j0SAVKweC2EUxNddKvdThGIycB+OSg/gMHDqC1pl27dhw9epQXX3wRwzC4++67vSZRE0JcnDF4NCHx8VT48B8C4V2Uw4EaOpaYoWOJriih+P1/oTetRm9YAR27oNIzUIPHoIL9d+yPvzBXfQJKYaRnXvK1KigYglr+nBpKMbBtBAPbRnCyopYleSUsyyth8+EKkiIcTE11MiHFSVSw76084fa0hQULFjQUgX399ddJSUmhe/fuMsNSCB+ii/KplzUMRTPIr6ylKK49xi33Y8x6FXXTvVB7Gv3a3zAfuR1zwTz08SNWhykuxhHkdpKl9+5CH8ht5oAuLjHCwa39Eph/TQq/HNmG2FA7r36ez53v5fHMxqPkFJzClyp7ud1DVlZWhtPp5PTp0+zdu5df/vKX2Gw2KQwrhA8x58+m1OEAqUMmPOzpDUdxOPJ5PL0NKjQMNe4KdHom5H6JXp2FXrUIvfxD6N4XIz0D+g51VXsXXsMYNcnt1+pP10JMvFfclnbYDMZ0imJMpyi+Lq4+s35mGSv3l5ESG0xGagxjOkURbPfu0hluJ2RRUVEcP36cgwcPkpKSgsPhoKampjljE0II4cOUUq7aZWm90KXF6HXL0GsWY74wE5xxrrIZoyehPFw+QTQ/dcOdjZqV2VI6xYRw35AkftQ/gewDZWTllPDc5uO8su0k4ztHMzXNSbso77x97nZCdt111/Ff//VfGIbBww8/DMDOnTvp2LFjswUnhBDCP6joGNQVM9AZ18GOTzGzs9Af/gv9yQLoN9Q1bqlr72avzC4uTG/fit7zBcYP7rnka5UF48caI8xhIyMthqmpTnbnn2JxTglZucV8tLeYPq3CyEhzMqSdd80GdjshS09PZ/jw4QAEnxmcmZqaykMPPdQsgQkhhPA/yrBBv6HY+g1FnzyKzl6CXr8c87MNkNQOlZ6BGj4OFRZhdaiBJyQUomPQpokyLnx7T5smbNsA7TujWnn3UllKKXomhtEzMYy7TiWybF8JS3JL+NPao8SG2vnNJBtpXpKXNWoFz+DgYEpLSykpKWmmcIQQQgQKldgGdcMd6KtvQn+6Hr16Efqtv6Pfe921qHl6BqpDitVhBgzVtReqqxu1RU/XYG5chWG3g5cnZGdzhtq5oVc81/aI47OjFWTllJAUFQz6lNWhAY2oQ/bFF1/wwgsvnDcZW7BggafjuiipQ+ZdpL18h96+haioKMqTu1kdik+Qa9t9Ww6XExUVRbeoy5vVpr/Z51o/c/NqOH0aOndFjc1ADR7lWq7HT/j6taXragF8fk1Tb6pD5nZC9uCDD3LllVeSnp5OUJC1/1NIQuZdpL18i5wv90lbNY4n20tXVaA3rERnZ8HxIxARiRo5ETVmKiqxtUeOYSVvvLb00YPoFR+hpl6HSkiyOpwW4U0Jmdu3LCsqKpg0aZIMuBTCh+njh6mrqYTgcKtDEX7mcFkNlbYqPHVlqbAI1MSr0BOuhK92uNbPXPYBeun70LO/axJA74GuMWnCM0JCIbG1a13Li9AlReicXaju/Vq8Ur8/czshGz9+PKtWrWpY21II4XvMN+ZQJnXIRDN4YfNxHI5CHk/37JgipRR074ute190cSF67VL02iWYzz0BsQnflc6IivHocQORik1ATbn20i8sLkBvzkZ1SgVJyDzG7YQsNzeXRYsW8cEHH+B0Os957ve/ly93IYQQzUvFxKGu+iE68wbYscXVa/b+P9AfvYUaMByVngmpPeROTjNTyWkY9z8K0s4e1ageMukdE0IIYTVlt8OAEdgGjEAfP4zOXoxevwK9dS207eiaBDAsHRUaZnWoPkXX1aL/ORfVbyiq75CLvlZWWfC8SyZku3btAlwD34QQQghvopLaoW68Gz39FvTWta4lmv41F/3ua6jh6a7krF0nq8P0DTY7tO0AUc6Lvkx/sw997JCrNIn0knnMJROyF1544aLPK6V47rnnPBaQEEII0VgqOAQ1ahJ65ET4Ote1fub6FejVWdClByo9AzVgBMrh22UampNSCjXx6ku+Th89iN6+GWNYevMHFUDcLnvhTaTshXeR9vIdevcXREVHU9422epQfIJc2+774lgl0dHRJIfVWR1KA11Rht5wJinLPw6R0ahRk1Bjp6LiEi2NzdevLa21X/SO+WTZCyGE71M9+hEcH0+5D/8hEN6pX+tw4uOdXpVkqIgo1ORr0BOvhj3bMVcvQi9+D734Xeg9yFU6o2f/iy4TFGjMD98Eux0j84aLvs4fkjFvIwmZEAFEH9xPbXkRRMZaHYrwM/uLqinSFcR64d9pZRjQsz+2nv3RhfnoNUvQ65ZiPvt7SEhy9ZiNmCg1tcA13u5Sdci2bwHDhuo9sGWCChCSkAkRQMwF8yiXOmSiGcz/7AQOR5HH65B5mopLQF1zC/rKG9Gfb3KNNXvnVfT7/0QNGoVKz3At1xSgPUBqwPBLvkYfyAGbXRIyD5OETAghRMBRdgdq8GgYPBp95CA6exF64yr0plXQPhmVnumaRRgcYnWoXseYfovVIfgluXEuhBAioKm2HTBuug9j1iuoW+4HrdFvPI/569sx33wJfeyQ1SG2GHPLGsyXZlkdRkCSHjIhhBACUCFhqLFT0WOmwL49rtuZaxajV34MXXtjjMuEvkNdhWn9lGrVBt1zwEVnUZqrFqE6paKSU1s4Ov/mv1eVEEII0QRKKVftsi490DPuQq9fjs5ejDn3TxAdixo92fVPrP8VTFcdu6A6drng87q+HvbvhegYkITMo6QO2Xn4en2Ylibt5Tt03h6inU7K4ltbHYpPkGvbfXvyq3BGO2kddNrqUJqFNuth1zbM1Vmw6zPXOo59h7hKZ3Tr0+jSGd58bX2bFgTCxAapQ3aWjz/+mJUrV6KUon379tx///0EBQVZHZYQfkl16U5QfDx46R8C4bu6J4QRHx/ltUnG5VKGDfoMxtZnMDr/+JnSGcswP98ErdqeKZ0xARUeYXWol0Uf3If54ZsY198OSe2sDiegWDqov6ioiKysLGbOnMlTTz2FaZps2LDBypCE8Gs6bw+nv9ppdRjCD+3Jr2Ln0TKrw2gRKiEJ47rbMP78CuquX0BEJPrf8zEfuR3z1WfQX+daHWLTRcWgBo6EsPDzPq1LCjGXf4AuzG/hwPyf5T1kpmly+vRpbDYbp0+fJiYmxuqQhPBb5sI3qJA6ZKIZ/OOLfByOEq+vQ+ZJyuFADUuHYenoQwfQqxehN2ej16+ATqmo9EzU4FGooGCrQ3Wbcsaiho+78AtOnYJDX0NPqUHmaZYmZLGxsVx55ZX85Cc/ISgoiL59+9K3b9/vvW758uUsX74cgJkzZxIf37wDKe12e7Mfw59Ie/mOIocDpZScLzfJte0+h+NoYF9b8fHQfzBmZQXV2YupWryQ+lefgXdeJmT8FYROuQZ7m/YNL/fma0ubJmgTZTtPihAfD737tXhMzcWbzoOlCVlFRQVbt27l+eefJywsjL/+9a+sWbOGMWPGnPO6iRMnMnHixIbt5h6j4M2DLb2RtJfvqK+txeFwyPlyk1zb7quVa+s7Q9LRg8di5HyJXr2Iqk/epurDt6BHP9ckgD6DSWjVyivbStfWYs6diTFiAmrgCKvDaXYyqP+MnTt3kpiYSFSUa/2woUOHkpOT872ETAghhPAlSino2gvVtRe6tBi9dil6zRLMOU9CTDwVU69BDxiJcnrZurJ2O2rIGDirN+9set9X6H1focZP8+t6bFawtDXj4+PJzc2lpqaGoKAgdu7cSUpKipUhCSGEEB6lomNQ025EZ1wPO7dirsqi8s2/w79fRvUbhhqXCWm9vKLMhFIKNXTshV9QVQHHD19yAXLReJbXIfv3v//Nhg0bsNlsdOrUifvuuw+Hw3HR90gdMu8i7eU79MH9OGOclEZ62a9yLyXXtvv2F1XjjHESq6qtDsUnOE+fouiDN9HrlruSnNbtUWMzUMPHoS4ww7Gl6Pp6qK/zqckITeVNtywtT8iaQhIy7yLt5VvkfLlP2qpxpL3c921b6dM16E/XoVdnwYEcCA5xLWo+NgPVobMlsZkL5kNoKMZVN1ly/JbkTQmZ3AAWIoDo3V9QEx0NbZOtDkX4mS+OVRJdZSc5zOpIfIsKCkaNmAAjJqC/znWtn7lpFXrNEkjphkrPQA0ciXK0XMF01W/IBW9JmlvWQF0txogJLRZPoLC0MKwQomWZn/ybyrdftToM4Yfe3lXAa1sOWh2GT1OdUjFu/xnGn19F3XgXVJSj58/GfOROzHdeRecfb5k4uvZGdelx/icrK6AiMAoAtzTpIRNCCCG8iAqPQE28Gj3hKtizHTM7C73sffTShdBrIMbYDOg9wLWcUzPQdbVQU3PeZaCMcZnNckwhCZkQQgjhlZRS0KMfth790MWF6LVL0GuWYj73fxCXiBozBTVqEirK6dHj6s3Z6B1bsf3kNx7dr7g4SciEEEIIL6di4lBX3YTOnAHbN2OuzkIvfAP94ZuuMWbpGdClu0dKZ6jOXVFRMWitv7c/8+MFqOQ0VM/+l30ccS5JyIQQQggfoex2GDgS28CR6GOH0NmL0RtWordkQ9uOqPRM1LCxqJCmz65QrdtD6/MXhqWmGupqm7xvcWFS9uI8ZOp240h7+Q59/DAxMTGUBFtb58hXyLXtvsNlNcTExBBeX2V1KD7Bk9eWrqlGb1mDXr0IDu6HkFDUsHGo9AxU246N319dHZyqhNAwlP3idUF9nZS9EEJYQiW1wx4fD5JkCA9rFxVMfEwYBQWSkLU0FRyCGj0ZPWoSHMhBr16EXrfMlaCl9XTVNBsw3P3k6sjXmB++iXHDHZDUrnmDFw0kIRMigOjtW6iJioLkblaHIvzMlsPlRJUpukVZHUngUkpB566ozl3RM+5Cr1+Bzs5C//0v6Mho1OgprokAcQkX31FcIsaEafAfkwV0SRF65Ueu3rc2HZrvgwQoSciECCDm0vepdDjgod9bHYrwMx/sKcLhKOfxdPduz4jmpSKiUFOuQU+6GnZ/gbl6ETrrHXTWO9BnEEZ6JvTohzK+X45URURBj/MM2jfNMy+QEqbNQRIyIYQQwk8pw4BeA7D1GoAuzEevWYJeuwRz+xZISHLdzhw5wZWEnaG1hrISCApGhX43OUDFxqOuvc2CTxEYJM0VQgghAoCKS8C45haMP7+MuudXEBOHfucVzF/fgfnybPT+va5krK4O8/XnYPfnVoccUKSHTAghhAggyu5ADRkDQ8agj3zjGme2cRV64yrokIIaOxU1diq07XTO+3TubvT2zahpP0CFhFoTvB+ThEwIIYQIUKptR9RN96Gv/RF6U7ZrhuYbz0NoOIwYD2MzUK3PzLQ0DLA5wC6pQ3OQOmTnIbWHGkfay3foonxiY2MppnnWwPM3cm27L7+yltjYWGw15VaH4hO89drSWkPeHsxlH8COLVBfD117u9aw7DvUVZjWj0gdMiGEJVRsAjapQyaaQUK4g/jIYAokIfNpSilI7YH6fCN6wpWo8Ej0miWYc/8E0bGo0ZNd/8TGWx2q35GETIgAYm5dS3VkJHTrZ3Uows+s/bqMyCJNv9jLX0tRWE+NGI+y2VGt26OnXgs7t2EufB398VvoRf+GfkMxxmZA974eWT9TSEImREDRq7OocjgkIRMetzi3GIejgn5Sh8wvqHbJ3/23YYO+gzHqTmMeP4w6dQq9fhnmto3Qqi0qfSpq+ARUeIR1AfsBSciEEEIIcQ5dUQYV5aiktg2PqYEjG0af6qtvQn+2Hr06C71gPnrhG6jBY1DpGahOqZbE7OskIRNCCCHEOfT2LejtW7Dd/+h5n1eOINSwcTBsHPrgflfpjE2r0euXQ6dUVHomavAoVFBwywbuw6QwrBBCCCHOobr3xZh2I2cXYjDfeQVzc/b3X9uhM8atP8WY9SrqBz+Gmmr0q8+4Cs7+ez76RPNWRvAX0kMmhBBCiHOo2ASI/Y9FyGMTzlli6XvvCQtHTZiGHn8F5Oxy3c5c+TF62QfQoz9Gegb0GYyySdmd85E6ZOfhrfVhvJW0l+/Q5WXExcVSdLrO6lB8glzb7iurriM2Lo66ylKrQ/EJ3n5t6eoqKDgJia0v67ajLilCr1uKXrMUigsgJh41ZoqrdEZ0jAcjbhpvqkMmtyyFCCAqMgojyml1GMIPRYXYcYY6rA5DeMqJo5gL34Ci/MvajXLGYkz7AcYf/45x/6PQuh36g39i/tedmC/+Gb13Fz7YL9Qs5JalEAHEXL+CU5ER0Geo1aEIP7NiXwkRJ+sZmii3o/xCqzYY19wCMa4CsLqsBP3ua67erZRujd6dstmg/zBs/YehTxx13c7csAL96Tpo3R6VnoEaNg4VFu7hD+I7JCETIoDoDSs45XBIQiY8buX+UhyOKoYmSh0yf6BCwuCsWmQYBrRPBg/UGlOt2qBuvAs9/Rb0p2tdydmbL6Hfex01dCwqPRPVPvmS+/E3kpAJIYQQ4hy6vh6OfAPRMajoGFREFGriVR49hgoORo2cCCMnor/OdSVmG1eh1yyBlG6o9EzUwJEoR2DcCpcxZEIIIYQ4lzYxP/gn5O1ukcOpTqkYt/8MY9YrqBl3QXkZev5fMR+5A/OdV9H5x1skDitJD5kQQgghzmWzY1x7GzhdMyF1zi702mWoGXeiIqOb7bAqPBI16Wr0hCvhqx2Y2VnoZe+jly6EXgNdpTN6DXAt5+RnJCETQgghxDmUUtC2w3cPRERBciq0UOV9ZRjQox+2Hv3QRQXotUvRa5dg/u3/IC4RNXYqauRElB/NGpc6ZOfh7fVhvI20l+/QNTXEx8dRWF5hdSg+Qa5t99XUmcTFxVFRWmx1KD7BF64tfeQgGArVur3VoQCg6+pg+2bMVYtg706w21EDRqLGZUBKd1cS2UjeVIdMesiECCAqOBgVHAKSkAkPC7YbhDhsyJXlP/T6ZRASirrqJqtDAUDZ7TBwJLaBI9HHDqGzF7tKZ2zJhnadUOmZrlmaIaFWh9ok0kN2Hr7wy8WbSHv5DnPVIiIiwqkaPNbqUHyCXNvuW5RTTER4BGPaBsaMuMvlC9eWLsp39UJFxbh6pY4dxLjpPqvDOoeuqUZvzkavXgSHDrgSyOHjUGMzUWffcr0A6SETQlhCf7qOaocDJCETHrb+mzIcjlOMaSt1yPyFOmstS9WmA9oLi7aq4BDUmCno0ZNh/1706kWu8WarFkFaT1R6Jqr/MJTd+38oSEImhBBCiO/RBSeguBCV2gPVtReNH6HVcpRSrtplKd3QM+5Gb1juqmv20ix0lBM1arJrlYG4hEvvzCKWJmRHjx5l9uzZDdsnT55kxowZXHHFFRZGJYQQQgi9dxd6+2ZsqT3QWjdp0LwVVGQUasq16EnT4cvPXaUzst5GZ70DfQdjpGdC976umZxexNKErE2bNsyaNQsA0zS59957GTJkiJUhCSGEEAJQ/YagevZDa43+xwvo1m0xJl5tdVhuU4YBvQdi6z0QXXACvWYJet0yzC82Q0ISKj0Dc9oMq8Ns4DW3LHfu3ElSUhIJCd7bnSiEEEIEChUe+d1Gz34Q0XwFYZubim+FuvZH6Ct/iN62wXU78+1XqGnbAXoOtDo8wItmWc6ZM4fOnTszderU7z23fPlyli9fDsDMmTM5ffp0s8Zit9upq6tr1mP4E2kv3yLny33SVo0j7eU+X2grs6yE2gO5ONJ6YIR634D+y1X7dR4hHTtTr5r31mVQUJBbr/OKhKyuro57772Xp556CqfTecnXS9kL7yLt5VvkfLlP2qpxpL3c5wttpb/Jw/zwTYzr74DE1iib/y1X5E1lL7xiRNvnn39OcnKyW8mYEEIIIVpAmw4Ytz0IEVGYc55E7/zM6oj8mleMIVu/fj0jR460OgwhhBBCnKEcQeAIQtdUo4aOhVatrQ7Jr1neQ1ZdXc2OHTsYOnSo1aEIIYQQ4gxdV4vetQ3KSjCGjEElStHf5mR5QhYSEsLLL79MWFiY1aEIIYQQ4izmqk/QB3JcyZn1Q879muUJmRBCCCG8kM2OcfvPUVFOzBdmQuFJqyPya5KQCSGEEOJ7lFKoyChIbI0xYjxERFkdkl/zikH9QgghhPA+OmcX2OyogTLxrrlJQiaEEEKI89JfbAbDgPbJqKBgq8Pxa3LLUgghhBDnpa6+GeIS0a8+a3Uofk96yIQQQghxXio4BNJ6QUKS1aH4PekhE0IIIcR56cMH4MRRVC/vWIDbn0lCJoQQQojz0t/sx1y/HF1TY3Uofk8SMiGEEEKclxo6BhWXgF620OpQ/J6MIRNCCCHEeSm7A4aMBYfD6lD8nvSQCSGEEOK8dFEBuuA4xLeyOhS/JwmZEEIIIc6vvAS9dR0UFVgdid+ThEwIIYQQ55fUzvXv/GPWxhEAZAyZEEIIIc7PZsOYdDUktLY6Er8nCZkQQgghzkvZHdCtj9VhBAS5ZSmEEEIIYTFJyIQQQgghLCYJmRBCCCGExSQhE0IIIYSwmCRkQgghhBAWk4RMCCGEEMJikpAJIYQQQlhMEjIhhBBCCItJQiaEEEIIYTGltdZWByGEEEIIEcikh+w8/vu//9vqEHyKtJdvkfPlPmmrxpH2cp+0lXfwpvMgCZkQQgghhMUkIRNCCCGEsJgkZOcxceJEq0PwKdJevkXOl/ukrRpH2st90lbewZvOgwzqF0IIIYSwmPSQCSGEEEJYTBIyIYQQQgiL2a0OwB0FBQU8//zzlJSUoJRi4sSJZGZmUlFRwezZs8nPzychIYGHH36YiIgI1q5dywcffIDWmtDQUO6++246deoEwJw5c9i2bRvR0dE89dRTFzzmF198wSuvvIJpmkyYMIHp06cD8Pzzz7N7927CwsIA+OlPf9qw77MtXryYTz75hBMnTjBv3jyioqIA+PLLL/nzn/9MYmIiAEOHDuX666/3XGPhm+317LPPsm/fPux2OykpKfz4xz/GbrdfNDZ/4U3nS2vNW2+9xaZNmzAMg0mTJpGZmfm99588eZKnn36a8vJyOnfuzIMPPojdbmf37t289tprfPPNNzz00EMMGzZM2uoCbfXtZ6msrMQ0TW666SYGDBgQ8O1l1XenL7aVv31vetM5eOyxxzh16hQAZWVlpKSk8Mgjj3zv/R79LtQ+oKioSO/bt09rrXVVVZX+2c9+pg8dOqTfeOMNvXDhQq211gsXLtRvvPGG1lrrr776SpeXl2uttd62bZv+zW9+07CvL7/8Uu/bt0//4he/uODx6uvr9QMPPKCPHz+ua2tr9a9+9St96NAhrbXWzz33nN64ceMlY96/f78+ceKEvv/++3VpaWnD47t27dJ//OMfG9cAjeSL7fXZZ59p0zS1aZp69uzZesmSJZeMzV940/lauXKl/tvf/qbr6+u11lqXlJScdx9PPfWUXrdundZa6xdffLHhfJ04cUJ//fXX+m9/+5tb572x/Kmt5s6d2/Dfhw4d0vfff3+T2uRifLG9rPru9MW28rfvTW86B2ebNWuWXr169Xn34cnvQp+4ZRkTE0Pnzp0BCA0NpW3bthQVFbF161bGjh0LwNixY9m6dSsAXbt2JSIiAoDU1FQKCwsb9tWjR4+G5y4kLy+PpKQkWrVqhd1uZ8SIEQ37dldycnLDL7mW5ovtNWDAAJRSKKXo0qVLQwwXi81feNP5Wrp0Kddffz2G4fpqiI6O/t77tdZ8+eWXDb/40tPTG96fmJhIx44dUUo1uT0uxp/aSilFVVUVAFVVVcTExDStUS7C19oLrPvu9MW28rfvTW86B9+qqqriyy+/ZPDgwd97v6e/C33iluXZTp48yYEDB+jSpQulpaUNX2JOp5PS0tLvvX7lypX079+/UccoKioiLi6uYTsuLo7c3NyG7TfffJN33nmHXr16cfPNN+NwOBq1/5ycHH79618TExPDrbfeSvv27Rv1/sbwtfaqq6tj7dq13H777R6JzddYfb5OnDjBhg0b2LJlC1FRUdxxxx20bt36nPeXl5cTFhaGzWYDIDY2lqKiokbF4Am+3lY33HADTzzxBIsXL6ampobf/e53jYqtsXyhvS6lpb47fa2t/PF70+pz8K2tW7fSq1evhmE3Z/P0d6FP9JB9q7q6mqeeeorbb7/9e43z7a+Es+3atYtVq1Zx8803eyyGm266iaeffpo//vGPVFRU8MEHHzTq/cnJycyZM4dZs2YxdepUZs2a5bHY/pMvtte8efPo3r073bt3b/bYvI03nK/a2locDgczZ85kwoQJvPDCCx7btyf5Q1utX7+e9PR05s6dy29+8xv+9re/YZqmx+I7mz+0V0t9d/piW/nb96Y3nINvrV+/npEjR3p8v+fjMz1kdXV1PPXUU4wePZqhQ4cCrm7c4uJiYmJiKC4ubhj8CfDNN9/w4osv8pvf/IbIyMiL7rugoIA//elPAEyaNIlOnTqd0/VZWFhIbGwsQEOW7nA4GDduHB999BEAf/jDHygpKSElJYX77rvvgsc6++IaMGAA8+fPp6ys7JzYPcEX2+vtt9+mrKyMH//4x+ccrzGx+SpvOV9xcXENxx8yZAhz5swBzj1f9957L1VVVdTX12Oz2SgqKmp4f0vwl7ZauXIljz76KABpaWnU1tZSXl5+wdtTTeVL7WX1d6cvtpW/fW96yzkA12D+vLw8fvWrXzU81pzfhT6RkGmtmTt3Lm3btmXatGkNjw8aNIjs7GymT59OdnZ2wz3egoIC/vKXv/DAAw/Qpk2bS+4/Pj7+nF9b9fX1HDt2jJMnTxIbG8uGDRv42c9+BtBwUWit2bp1a0OX+W9/+1u3PktJSQnR0dEopcjLy8M0TY//z+KL7bVixQq2b9/OY4891jBuoimx+SJvOl+DBw9m165djB8/nt27dzfs/z/PV8+ePdm0aRMjR45k9erVDBo06LLbwR3+1Fbx8fHs2rWL9PR0Dh8+TG1trcd/mPlie11Ic393+mJb+dv3pjedA4BNmzYxYMAAgoKCGh5rzu9Cn6jU/9VXX/HYY4/RoUOHhq7KH/7wh6SmpjJ79mwKCgrOmQo7d+5cNm/eTHx8PAA2m42ZM2cC8PTTT7N79+6GX6IzZsxg/Pjx3zvmtm3beO211zBNk3HjxnHttdcC8Pvf/56ysjIAOnbsyI9//GNCQkK+9/5Fixbx4YcfNnyJ9O/fn/vuu4/FixezdOlSbDYbQUFB/OhHP6Jr164B314/+MEPSEhIaHju2yntF4vNX3jT+aqsrOTZZ5+loKCAkJAQ7rnnnvNOlz9x4gRPP/00FRUVJCcn8+CDD+JwOMjLy+Mvf/kLlZWVOBwOnE4nf/3rX6WtztNWhw8f5sUXX6S6uhqAW265hb59+3qsrXy1vaz67vTFtvK3701vOgcAjz/+ONOnT6dfv34XjNmT34U+kZAJIYQQQvgznxrUL4QQQgjhjyQhE0IIIYSwmCRkQgghhBAWk4RMCCGEEMJikpAJIYQQQlhMEjIhREB4/vnneeutt6wOQwghzksSMiGEOMvjjz/OihUrrA5DCBFgJCETQgghhLCYTyydJIQQjXXgwAHmzp3LsWPH6N+/f0Pl74qKCp577jlyc3MxTZOuXbtyzz33EBcXx5tvvsmePXvIzc3l1VdfJT09nbvuuosjR47w8ssvs3//fqKiorjxxhsZMWKExZ9QCOFPpIdMCOF36urqmDVrFqNHj+bll19m+PDhbN68GXCtl5eens6cOXOYM2cOQUFBzJ8/H3At09K9e3fuvPNO3njjDe666y6qq6t54oknGDVqFPPmzeOhhx5i/vz5HD582MqPKITwM5KQCSH8Tk5ODvX19VxxxRXY7XaGDRtGSkoKAJGRkQwbNozg4GBCQ0O59tpr2bNnzwX3tW3bNhISEhg3bhw2m43k5GSGDh3Kxo0bW+rjCCECgNyyFEL4neLiYmJjYxtuUwINCxDX1NTw2muv8cUXX1BZWQnAqVOnME0Tw/j+b9T8/Hxyc3O5/fbbGx6rr69nzJgxzfshhBABRRIyIYTfiYmJoaioCK11Q1JWWFhIUlISH330EUePHuXJJ5/E6XTy9ddf88gjj6C1BjgniQOIi4ujR48e/O53v2vxzyGECBxyy1II4XfS0tIwDIOsrCzq6urYvHkzeXl5AFRXVxMUFERYWBgVFRW8/fbb57w3OjqaEydONGwPHDiQY8eOsWbNGurq6qirqyMvL0/GkAkhPErpb38WCiGEH9m3bx8vvvgix48fp3///gC0bt2ayZMn8+yzz7Jv3z5iY2OZNm0af//733nzzTex2Wzk5OTw/PPPU1ZWxujRo7nzzjs5evQor732Gnl5eWit6dixI7fddhudOnWy9kMKIfyGJGRCCCGEEBaTW5ZCCCGEEBaThEwIIYQQwmKSkAkhhBBCWEwSMiGEEEIIi0lCJoQQQghhMUnIhBBCCCEsJgmZEEIIIYTFJCETQgghhLDY/wfneMjjO4SbcgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pred = df_min_rel.assign(\n",
    "    installs_hat_0=twfe_model.predict(df_min_rel.assign(relative_days=-1))\n",
    ") \n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "[plt.vlines(x=cohort, ymin=7, ymax=11, color=color, ls=\"dashed\") for color, cohort in zip([\"C0\", \"C1\"], cohorts)]\n",
    "sns.lineplot(\n",
    "    data=(df_pred\n",
    "          [(df_pred[\"cohort\"].astype(str) > \"2021-06-01\") & (df_pred[\"date\"].astype(str) >= \"2021-06-15\")]\n",
    "          .groupby([\"cohort\", \"date\"])[\"installs_hat_0\"]\n",
    "          .mean()\n",
    "          .reset_index()),\n",
    "    x=\"date\",\n",
    "    y = \"installs_hat_0\",\n",
    "    alpha=0.7,\n",
    "    ls=\"dotted\",\n",
    "    color=\"C0\",\n",
    "    label=\"counterfactual\",\n",
    ")\n",
    "sns.lineplot(\n",
    "    data=(df_pred\n",
    "          .groupby([\"cohort\", \"date\"])[\"installs\"]\n",
    "          .mean()\n",
    "          .reset_index()),\n",
    "    x=\"date\",\n",
    "    y = \"installs\",\n",
    "    hue=\"cohort\",\n",
    "    legend=None\n",
    ")\n",
    "plt.ylabel(\"Installs\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b780c654",
   "metadata": {},
   "source": [
    "![img](data/img/did-saga/awful.jpeg)\n",
    "\n",
    "These counterfactuals are a bit better, though. We can see that they fall above the realized $Y_1$ outcome. As a result, we will at least estimate a negative effect, as we should. To see that, we can plot the estimated effects by first extracting the parameter associated with each dummy and then subtracting from them the parameter associated with relative day -1 (the baseline)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a68f996c",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAEJCAYAAADYTyDcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABIlUlEQVR4nO3deXxU1fk/8M+ZmezJLFkgkgRCWERRcQEFBFyI/bUurVql4NJirVpxq1qrVpEqRaiIW6VqK6DSb1WsouJaEUEFLAiiFTe2hLCGZJbsy2TO748792YGMpOZZJI7N/N5v168mLkzuXnmTJYn55z7PEJKKUFEREREujDpHQARERFRImMyRkRERKQjJmNEREREOmIyRkRERKQjJmNEREREOmIyRkRERKQji94BdMe+ffv0DgG5ubmoqqrSO4w+gWMZOxzL2OFYxg7HMnY4lrHVG+M5YMCAkI9xZoyIiIhIR0zGiIiIiHTEZIyIiIhIR4beM0ZERETxRUqJpqYm+Hw+CCH0DiciBw8eRHNzc7fPI6WEyWRCampqVK+dyRgRERHFTFNTE5KSkmCxGCfFsFgsMJvNMTmX1+tFU1MT0tLSIv4YLlMSERFRzPh8PkMlYrFmsVjg8/mi+hgmY0RERBQzRlma7EnRjgGTsRDk9m/he/V5SF+b3qEQERFRH8ZkLARZsRPyvVeBuhq9QyEiIqIYWLFiBc444wxccsklAIAZM2agtLQUTz/9dFTn8Xg8eO6552IWV+Iu6nZCWB2QAOBxA1aHztEQERFRd7300kuYP38+Tj31VFRWVuLLL7/E2rVrYbFY4PV6Iz5PTU0NXnjhBUyfPj0mcTEZC8VmV/73uICiwbqGQkRERNF59dVXsXjxYrS0tOCkk05Cfn4+NmzYgNtvvx0/+tGPsGbNGhw4cADnnHMO5s6di9zcXNxzzz2orq5GWloa5s+fj6FDh+LQoUO46667UF5eDgCYO3cuFi9ejPLycpxzzjmYNGkSZs6c2a1YmYyFYrUDAGSNC9yKSEREFD3fS/+ArNgV03OKosEwTb0m7HO2bduGN998E6+//jqSkpJw9913o6ioCKNGjcLMmTMxatQoTJ8+Hb/61a/wwQcfwGKx4OKLL8a8efNQUlKCzZs34+6778Yrr7yCmTNnYuzYsVi0aBHa2tpQX1+PP/7xj/j+++/xwQcfxOQ1MRkLRV2a9Lh1DYOIiIii8+mnn+J///sfzj33XABK7bPc3NyQz6+vr8emTZtw3XXXacdaWloAAGvXrsXjjz8OADCbzbBarfB4PDGNl8lYCCI1DUhJBWpceodCRERkSJ3NYPUUKSUuvfRS3H333UHH1Y37h/P5fLBarTGb6YoWr6YMx2pX9owRERGRYUyYMAFvvfUWqqqqAAAulwt79uwJ+fysrCwUFRVhxYoVAJRkbuvWrdq5XnjhBQBAW1sbampqkJGRgbq6upjFy2QsHJsDssatdxREREQUheHDh+MPf/gDpk2bhtLSUkybNg0HDx4M+zFPPvkkXnrpJZSWluKss87Cf/7zHwDAAw88gHXr1mHy5Mn48Y9/jB9++AHZ2dkYM2YMzj77bMyePbvb8Qoppez2WXSyb9++Hj1/21PzgP0VMD+wMORzcnNztcybuodjGTscy9jhWMYOxzJ24nksGxoakJ6erncYUYm2tEVnOhqDAQMGhHw+Z8bCEDY7lymJiIioRzEZC8fqABrqIFtb9Y6EYkBW7odsadY7DCIioiBMxsKx+ctbcN+Y4cnWVvgeuAXyo3f0DoWIqE8z8O6nmIl2DJiMhSHUWmMsb2F8NS6guQmoOqB3JEREfZrJZIrp/iuj8Xq9MJmiS69YZywcfxV+7hvrA9xOAIDke0lE1KNSU1PR1NSE5uZmCGGMHjYpKSlobu7+NhYpJUwmE1JTU6P6OCZj4fj7U7IlUh+gJmFcciYi6lFCCKSlpekdRlT0vjqVy5ThZNmV/9kSyfC0GTHOjBERUZxhMhaGSEoCMrK4Z6wvUN/DGhc3lxIRUVxhMtYZq51V+PsCdUaspQVoatQ3FiIiogBMxjpjc3Bpqw+Q/g38ALhvjIiI4gqTsU4Iq4O/vPsCjwtISm6/TUREFCfiKhnz+Xz4wx/+gHnz5ukdSjt/SyTuMzI4jwsoLFZucw8gERHFkbhKxt555x0UFBToHUYwmwNoaQaauc/IqKSvDahxQxSVKPd5dSwREcWRuEnGqqursXnzZkyePFnvUIKpVfj5C9y4amsA6QMKBgImE2fG+gjfu/9G24O/1zsMIqJui5uir8899xyuuOIKNDaGnoFauXIlVq5cCQCYN28ecnNzezyu5qJBcAOwCR+SO/h8FoulV+JIBD01lq011XACsBYVo9aejeTmRtj6+HuWCF+X7n270Vy+AznZ2RBRth6JRiKMZW/hWMYOxzK29B7PuEjGNm3aBJvNhpKSEmzdujXk80pLS1FaWqrd741qudJfe9+zuwyiX+ERj+tdtbcv6amxlOW7AAC1Jgt8mTY0VR5Aax9/zxLh67Lt0AHA14aq8l0QWbYe+zyJMJa9hWMZOxzL2OqN8RwwYEDIx+IiGfv+++/x+eef44svvkBLSwsaGxvxxBNP4Oabb9Y7NG2ZUnrcbIlkUFpZC5uDpUr6EvV99biAHkzGiIh6WlwkY5dddhkuu+wyAMDWrVuxYsWK+EjEACAzy7/PyK13JNRVavJlc0BY7ZAVO/WNh7pNShnQb9QFoFjPcIiIuiVuNvDHK2EyKz0quenbuDwuID0TIilZmRmr9UD6fHpHRd3RUA94WwGAHTKIyPDiYmYs0MiRIzFy5Ei9wwhms7c3mibDkR6nkoQByrJzWxtQXwdkWfUNjLrOE9BRgVc6E5HBcWYsEqzCb2weF2DPBgAIm105xplOYwv844jvJREZHJOxCAh/FX4yKI8LInBmzH+MjEuqM2MmE99LIjI8JmORsDqAWjf3GRmQstE7cJnSrhznbIqxqQnYUUXcM0ZEhsdkLBK2gH1GZCwNdYDXC9iUZUotKeM+I2Nzu4DkFCDvKM6MEZHhMRmLhLq0xdkU43G3l7UAAKSmAcnJfC+Nzj/bKewOvpdEZHhMxiIg/Etb/AvcgPx7i4R/ZkwIoSTXfC8NTda4ldlOqwOoq4X0l7kgIjIiJmOR8F+Bx31GxqO9Z+rMmP829xkZnMepXJShXR3r0TUcIqLuYDIWCW2Z0q1rGNQF6gyYPSAZs9r5Xhqdv1yJ4BYCIuoDmIxFIi0dSErmpm8jcruAlFSI1HTtkGB/SkOTzc1AY0N7r1GAyTURGRqTsQgo+4zs/OvbiDxOrZyFxuoA6mogvV5dQqJu8gQ0fvfPjLFDBhEZGZOxSNkc/IFvQNLjai9roVL3GdVyn5Eh+b8PhS27PdHmzBgRGRiTsUixJZIxBVbf99OujuVMpzEFzIyJpCQgPZPLzkRkaEzGIsSWSAblcWp9KTVsiWRo2gy1mmTbHLzSmYgMjclYpLjPyHBkcxPQ1Bhc1gLQ7nPZ2aA8TsBsBjKylPtWOy+uISJDYzIWKfUXOvcZGUfgRu9A3GdkbG4XYHVAmJQfX4IX1xCRwTEZixD3GRmQO2CjdwCRlAykZ3CZ0qCUizKCi/hyZoyIjIzJWKQ4m2I4R+wtCsQLMozL35dSY3UAzY2QTY36xURE1A1MxiLFfUbGoy1TZh/5GDd9G5fHFTzbqbVEcusRDRFRtzEZixSbhRuPxwWYLUBm1hEPCW76NiTpbQXqaoJmxtgSiYiMjslYhERyCpCWwb++jcTjBGx2pYPC4WwO/vI2IvX7z37YnrHAx4iIDIbJWDRYa8xQOqy+r7LagaZGpfwFGYeng4sy/MuUkjOdRGRQTMaiYeU+I0M5/Kq7QFbOphhSR+VKMq2AMHGmk4gMK6JkbNu2bR0e3759e0yDiXeCl9Abi8d5RCsklVA3fXOm01C02a+AmTFhMgNWG99LIjKsiJKxP//5zx0enzNnTkyDiXssLmkYykbv2jDLlNz0bUgeJyBE+wU1qiw7JGc5icigLOEe9Pl8AAAppfZPdfDgQZjN5p6NLt7YHNo+I5GSqnc0FI42gxJimVIrVeJGB9v7KV55XECmFeLwnz3cz0lEBhY2GZs2bZp2e+rUqUGPmUwmXHTRRT0TVbwKLPyal69nJNQZ/96iw6vva7K4z8iIQl2UIawOyP0VOkRERNR9YZOxJ598ElJK/OlPf8L999+vHRdCwGq1Ijk5uccDjCfC6oAEmIwZgZpk2UPsGTOZlYSMS1vG4nZ2/J7693NKKTsuZUJEFMfCJmN5eXkAgMceewwmkwkWS/vTvV4vWltbkZSU1LMRxhNu+jYM6e9Lqe0N64jVwY4KRuNxQRQOOvK4zQ60eYGGOiDjyCK/RETxLKIN/HPmzMHOnTuDju3cuTMBN/D79xlxaSv+eVwdb/QOZLNzZsxApM8H1Lo7vihDTbqZXBuW9LXB9/5rkA31eodC1OsiSsbKy8sxbNiwoGNDhw5FeXl5jwQVt7Jsyi94lreIfx5nxxu9AygtkfjL2zDqaoC2tg5nOwWr8Btf+U7Ifz8HueW/ekdC1OsiSsYyMjLg8XiCjnk8HqSkpPRIUPFKmM1KgUnOjMW9sNX3VValJVLgVcIUx9Tq+x3tGVNnrZlcG5enWvmfP18pAUWUjJ122ml4/PHHsXv3bjQ3N2P37t148sknMW7cuJ6OL/7YuM/IEDyukJv3NTYH4PUCXBYxho6q76vU/ZycGTMsbZ8n30NKQGE38KumTp2KF154AX/84x/R2tqK5ORknHnmmUGlLxKG1cEfFkbgcXa80TuQVqrEBWRk9nhI1D3aH0EdzXimZQCWJC47G5mabPPnKyWgiJKx5ORk/OY3v8HVV1+N2tpaZGVlJezl48JmhzywR+8wKAzpa1N+oHeyTCls/lIlHhdwVFFvhEbd4Q49MybUizW4xGVc/veXnRQoEUXcKHzv3r149dVX8corr0AIgX379iXeBn6A+4yMoK4G8PlCV99X2bjPyFA8LiAtAyI5xF5Vm6O9dyUZjvZ9yGSMElBEydj69etx3333wel04uOPPwYANDY24oUXXohJEFVVVbj//vtx66234rbbbsM777wTk/P2CHWfUSP3GcUt/96TkNX3VepVebXuno2HYkK5KCNc3Tg7Z8aMzK1u4HfrGgaRHiJaply2bBlmzpyJ4uJirF+/HgAwaNAglJWVxSQIs9mMK6+8EiUlJWhsbMRdd92FE044AYWFhTE5f0yp+4w8biCd+4zikra3qJOZsfQMwGJhqRKj8DjDvqfC5oDc+X0vBkQxpX7f1tVAtrWFLUtD1NdENDPm8XgwaFDwZmghRMz2jTkcDpSUlAAA0tLSUFBQAKfTGZNzx5oI3PRNcUmGu+ougLLPyMFN30bhcYWf7bQ6tF/kZCzS6wVqPUotRymV20QJJKKZsZKSEnz88cc444wztGNr167F0KFDYx5QZWUldu3a1eG5V65ciZUrVwIA5s2bh9zc3Jh//s54i0tQDSDL50Vqbi4sFosucfRFsRrLutZm1APIHTIs9P4iv+rsXJia6uHoY+9hX/u6lFKissaNtKMGICvE62ooKEStlMhOMsOcHbvX3tfGUk+hxrKtqhJVUiJ5yNFo2bIBdjOQxDEPi1+XsaX3eEaUjF111VX485//jFWrVqG5uRlz5szBvn37cO+998Y0mKamJixYsADTp09Henr6EY+XlpaitLRUu19VVRXTzx8J6VP+r9m7G3VVVcjNzdUljr4oVmPp278HSM9AdU0tgNqwz23LyAIOVfa597CvfV3KhnqgpRmNyaloDvG6pEnpk+ss2wnhi93n7mtjqadQYyl37QAAtPYvBLAB7vJdEFmd7PlMcPy6jK3eGM8BAwaEfCxkMlZXV4fMTGVPVEFBAR577DFs2rQJp5xyCnJycnDKKacgNTU1ZkF6vV4sWLAAEydOxGmnnRaz88ZceiZg5j6jeBZR9X0/YbVD7vqhhyOibgtXY0yltUTisrPhqNX3Bw0BoJS3SMziSZSoQu4Zu+GGG7Tbs2fPRkpKCsaPH4+f/vSnOP3002OaiEkp8fTTT6OgoADnn39+zM7bE4QQSrVv7jOKX51ddRfI5gBqa5TaZBS//PsARWdXUwIsb2FAavV9UaTsHeYVlZRoQiZjycnJ2L17N3w+H7Zv3w4pJXw+3xH/YuH777/Hxx9/jK+//hp33HEH7rjjDmzevDkm5+4RVgck//qOX25n+F/agawOZe25tqZnY6JuCVt9X2XlzJhheZyAMAF5+UByMpMxSjghlykvvfRSrf0RoLRE6sjLL7/c7SBGjBiBZcuWdfs8vcbmAKoP6R0FdUBK6Z8Zi3CZ0mZvr8IfaQJHvS+CK2RFSgqQmsZZayNyOwGrTSlnYXVwGwglnJDJ2I9+9CNMnjwZbrcbv/vd7/DII4/0ZlxxTVjtkGXb9A6DOtJQD3hb2xtHd0abTXH3VEQUCx6XMmOSduSFPUHYO9aQgvZ5Wu2QLMRMCSbkMuU999wDs9mMnJwcjBkzBnl5eR3+S0g2B1Dj4T6jeKTNoER4JZbaEolLW/HNrfyy7rS2oc3O9lZG5K4G7O3JGBNqSjQhk7F9+/ahpaUFALBp06ZeC8gQrHZln1Ed9xnFHY/aCinSPWN2/8e5eyQcig3ZSfV9lfD3jiWD8bi071nBQsyUgEIuU44ZMwa33HIL+vXrh5aWFsyaNavD591///09Fly8ElaHf5+RW+dI6HARbfQOIFJSgZQ0/gKPdx4XMGBg58+zOYBvtvR4OBQ7WvX9wJmx+lq2RKKEEjIZmzFjBr777jtUVlZi+/btOOuss3ozrvim7kfiX2/xJ9K+lIFYqiT+eVwQx4zq/HlWO9BYD9nS3Gn3BYoTNW6lBVLAnjGtJZKdhV8pMYStwD9ixAiMGDECXq8XZ555Zi+FZABW7jOKWx5nZBu9A1kdkNyjErdkSzPQWB9Zgm0LuCAjt3+PxkUxom4t8Cde2hXONS4mY5QwwjYKf/PNNwEAZ599NgDgq6++Cnr8+eef76Gw4hz3GcUvT4QbvQNxZiy+qe9NBL+YhY1XxxqOWn0/cJkS4HtICSVsMvbqq68G3X/00UeD7q9atSr2ERmASE3jPqM4JbtQL4ybvuNcJNX3VSz8ajhq9X3t+1btpMBkjBJI2GRMShn2gzt7vE/jbEp8ivCquyA2B9BQD9na0jMxUfdEc1GGfz8nWyIZiFp9P8uu3FcTar6HlEDCJmOdLfVEtRTU13CfUXzyuCAirTGm4rJIXDti5iScTJvyP/9QMo7A6vvwrzwkp/D7kRJK2A38UkpUVlZqM2Ad3U9YNjuwr0LvKCiAbG4GGhuiX6a0+UuV1LiBnH49ERp1R40LMJuBTGunTxUWi/I8LlMahuyofRkLv1KCCZuMNTc346abbgo6dvj9RCWsDshvv+r8idR7oq2+r1KTN86mxCePE8iyQ5jCTuS3szm4TGkk7mrAnhN8zObg1eqUUMImY7FoAt5nWe1AQx33GcWTaKvvq/x7VWSNCwm88B63or4ow2rnzJiReFwQxcOCj2XZgcp9uoRDpIewyRiF4f/l4HM7ARH7YZRSAk2NylR9jRuodSt71GrcQK0n4HYNxPizYTr30pjHYDjqzJg9ymTMqu4zcsc0HIoRtwvIibwPrrA5ILd904MBUawcUX3fT1jtkNv5HlLiYDLWRWpLJJ/LCWSH32ck29qUopUN9cqepkblf9lYDzTUaQmXrPFoyRZq3IC3tYNPLICMLCDLpswAJCVDvvkvyJPHQeQXxv6FGki0rZBUwpIEZGZxNiVeeZwQJcMjf77VoXw/SZnYFxkZweHV91U2O1BXA+n1KvsAifo4fpV3lf8S+oa3l8GXmq4kWQ31kAHJlpZ4tTSHP5fZ4k+ulARLDBio3UaWHcJq99+2AVm2oH5tssYF373Xw7dsMcw339dTr9YYPE5lo3dGVvQfa3W0J3MUN2RbG1BXE317q9YW5fsvPaPHYqMYOKz6vka9wrnOc+R+MqI+iMlYV+XlAylpaPr4P/72OxlKCx7//yI7T/lFkJYefFx9XnqGciw9A0jP7PJf8MLqgDj/F5CvLIH83yaI40+J8Qs1ELcLsDoi3+gdyObg1VvxKNTMSTjWgCr8TMbim1p9/7BkW1jt7Vc4MxmjBMBkrItERhZMj/4Tubm5qPZ49I3l7PMh17wP37JnYTpmVMJO63el+r5KWO2QO76LcUTUbdFU3/drL1XiAvILeiYuigmthtwRM2Ms/EqJJeRv7euvvz6iEzz11FMxC8ZoRFISRFKS3mFAWJJg+sXV8P11NuRHb0Oc8zO9Q9KHx9n15tA2pSUS9xnFma7sA/T/IpceN6+OjXeHV99XBbRE4ntIiSBkMhZYT2z79u1Ys2YNfvKTnyAvLw+HDh3C+++/j0mTJvVKkBSB40cDx50MueIlyNPOUPaZJRqPC2LIiK59rNUBtLQAzY1Aanps46Iuk1rtuChLWwC8IMMIDqu+r2FXDEowIZOxY489Vru9aNEi3HPPPcjObv/r9KSTTsKDDz6ICy64oGcjpIgIIWCacjV8998M+cb/QVx5g94h9SrpbY1+o3cg/wUZ8LiZjMUTdRkrmj8uMjKVCzl4QUbc67D6PvwtkVJSmVBTwohop7PT6URqamrQsdTUVDidzh4JirpGHFUEcdZ5kJ/8B3L3Tr3D6V3qX9DRVt/302YS+Qs8vnhcQKY1qn2QwuRf9uIv8vjnrg79BxRbIlECiSgZGz16NP7yl7/gq6++wp49e/Dll1/i4YcfximnJPCVe3FKXDAVyMiC7+V/JFbvUP9G36ir76u0K/D4CzyeSI+za7OdbIlkDB7XkWUtVFa7UtyaKAFE9OfmNddcg1deeQX/+Mc/4HQ6kZ2djbFjx+LSS1n1Pd6I9EyIi66AXPo3YNNaYPQEvUPqMtnaAtTWAPW1SgHIulqgvkZZjqyrBeprIdXb7i72pVTZuOk7LoVYxuqU1d7ekYHiUqjq+xqrHTiwt1djItJLRMlYcnIyLr/8clx++eU9HQ/FgJhwDuRH78L3yhKYjh8DkZKiazxSSqXwrZo41dW0J1EByZWrpQltrmrtOWGL5aamAZlWfzcCK0T/AcqVlANLuhZkRhZgMvXKzJiUUunG4G9zpXRfcLd3XkhOgbjkqiM3NScijwviqKKoP0zYHIm3VG80ndSQE1Y75LatvRsTkU4i3ojx1VdfYe3atfB4PLjrrruwY8cONDY24rjjjuvJ+KgLhMkM09Rr4Hv4j5D/Wa4sXcaIlFLpKuCfmdJmrAKSKxkwc6Ud76i1kyo9E8jMgrRnA7ZsiIJipT2RmmhlWNvvZyq3hSW2JUWEyeSfTYk+GZNtbUBTQ3u7q7qA3qH+FleyNqDVVa0b8Ho7CkLZfF5XA+TlQ5x9fvdelMFJn09JjqPtNQooy861bkifr2tFgKnnhaq+r7LagbpatkSihBDRV/i7776Ld955B5MnT8Znn30GQJktW7JkCf785z/3aIDUNeLo4yBOOR3yvX9Dnj5Z6QhwGOlrA+rrg2anZGACVV8LWVvjf9x/vKEOaGsL8UlNwUlTbn+I4mH++8ox4f8fGVb//xkQJmUGKDs3F1VVVT05LOFZHZD7KyC3/Pewtlbt7a2keruhvv14c1Poc1osWlsr2BwQhcXKff8/ofYYtdqVMRIm+B69D/KNf0GeOgki09oLLzxO1fu/1rqyTGmzAz6f8gdBli3moVEMhKi+r1H3cdZ6AAer8BuRLN8BQEIMGqp3KHEvomTsnXfewcyZM9GvXz+88cYbAICCggLs27evR4Oj7hGXTIf8aiN8zzwEkV8YnGipiVWoTf5mizYLhYws4KiigERKSa6Edtv/vLQMY89C5PYDNq+Hb+Gc4OOWpKCWVkjPUGbwgo4pt0VaOpAZkGClpUddRNb0i9/Ad/8tkG/+C+Ky38bs5RlOF6rvq7Qq/B4Xk7E4JdV9niFmxoJaIjEZMyTfS/8ApA/mux7SO5S4F1Ey1tjYiNzc3KBjXq8XFk4dxzWR2x/igmmQb78M6apun50qyg1OtNTEKmAPFlLSEq4SvemKGcCZ5wYnXmkZvd5lQRQMgjjzx5Cr34Oc9GNlNi0RdaX6viro6tjiWEVEseRxdVx9X8XCr8bn0nGlw2AiyqaOOeYYvP7667j44ou1Y++++y5GjhzZY4FRbJh+8nPgJz/XOwxDEFk24JhReocBABA/uxxywyfwvfwsTLfNTrjEGOhi9X2V2k6HV8fGr1DV91XqFc5siWRIUkqt3RXbzHUuojWlX//619iwYQNuuOEGNDU14ZZbbsH69evxq1/9qqfjI0pIIiML4meXAd99BXzxmd7h6KM7M2NqRwXWjYtboarva9jWytjqa5ULlVpbgKZGvaOJexHNjDkcDsydOxc7duzAoUOHkJOTg6FDh8Jk5P1BRHFOTPox5Op34XtlMUzHnwKRlKx3SL3L41L23HWlNEtKGpCcwo4K8cxdDdhD7wUTKan+lkju3ouJYifwe6/GrWz7oJAiyqYeeughCCEwdOhQjBs3DsOHD4fJZMLDDz/c0/ERJSxhVkqUoOog5Adv6B1O73N3sfo+lF6tsDm0zgwUh9zO0GUtVGyJZFzugKLL/KOoUxElY1u3dlx4L9RxIooNccwo4KSxkO+8olyEkUA6XcbqjNUOySWuuCS9XuWK7s7eX6td+Togw5GBHTBq3brFYRRhlylffvllAMqVk+pt1cGDB5GXd2TtKiKKLdOlv4bvvhmQr70AcfWteocTE7KtTSmtElgsOKD1Fepqgb1lEMeP7vonsTmA/XtiFzTFjlp9P5KZMbZEMqaAmTFehNG5sMlYdbXyl7jP59Nuq3JzczFlypSYBbJlyxYsWbIEPp8PkydPxoUXXhizcxMZmcjLhzjnQsh3/w155k8ghozQO6Qg0tvaXrsuZFeG4MfRUB/6hJYkpcxKTj+IU8Z3OS5hdUB+/3WXP556UGfV9/3YEsnA3E6lbV1zM5cpIxA2GZsxYwYAYPjw4SgtLe2xIHw+HxYtWoR7770XOTk5uPvuuzF69GgUFhb22OckMhJx7iWQ61YppS7ueqjHiuvK5uagjgtKoeCARKq2BjKwI0N9bfgrpVJSg9tY5fYPqHHnP5Zl1W4j06r05ozFZfA2u9JFwtsa8/ZZ1E2dVd9XWR1siWRQ0uMCHLnKzwju++tURF/daiLW2NiI2tpapX6IX//+/bsdxPbt25Gfn6+da/z48di4cSOTMSI/kZoOcfEvIZc8BvnZaojxZ4d9vpQSsqkBqO1gtipgKVD6EyzU+5u2t7SEPmlaRnvClGVTGngHtL/SOjQEJlp6XgGqFn6t3A+ZkhbU3kprd9UQ3PKqozZYlUnJkEcVKcV3CwYp/w8YqFztR13SWfV9jVregi2RjMfjVN5fs1np1UthRZSM7dmzB0888QTKy8uPeOzwvWRd4XQ6kZPT/o2Wk5ODbdu2HfG8lStXYuXKlQCAefPmHdEVQA8WiyUu4ugLOJbhyfMvgWvtB2h7fSkyMjPhq/VA1nrg0/7VQNa44autQWWoZuQAIARERhZMVjtMWVaIowpgyrLClGWDKcsG4f/fZLXB5O+facq0Gm5mormoGG4Avlk3hn+iJQkiPQOmjEyI9EyY0jMgcvpBZGTAlJ4JNDehpWw7vJ9+ADQ3KS16hIC5/wBYBg2BZdBQWAaVwDJoCMz5haGLmJL2PV7X0ox6IZA7eAiEOfTXVVPhQHgA2E1AEn82BIn3n5eHaj1IPuYE+JKTIRvrkR3HsQL6j2dEP12fffZZjBw5ErNmzcKNN96IhQsX4l//+heGDx/e0/EFKS0tDVou1bWptF+u3s2t+xCOZefkz6fDN+9O1Pz1z8oBkymoXyhy+kEMGor03H5oNCd13EM0vb05u89/3hCt3xVeH+B29+Cr6hmyoBjikqsAIZR6ZekZR7S6Qlo6kJSsLYtKHDkW6telyecDqg4Ce8og95ajbW8Z2sp3oHnDp4D0j2RyMnDUQIiCQUBhcfv/6gxPglPH0rd/D2C1o9rlDvt8KZSvU/fuXRA2zowFiuefl1JK+JyH0JyaAaS1QO4pj9tYVb0xngMGDAj5WETJWHl5Oe69915YLBZIKZGeno4rrrgCt99+OyZNmtTtALOzs4MuEKiurkZ2djcuaSfqo0TJ0TDNeVq5Ei3TGrIReVZuLprj/IdfTxPJKRD/76LYnc9kAvodBfQ7CuLkcdpx2dIM7K+A3FMO7C2D3FMG+fUmYN2HUDd0iMt+C9NZ58YsFqOLuGyJ2taKV+MZi1p93+5QflbVuNkSqRMRJWNJSUloa2uDxWJBVlYWqqqqkJGRgbq6upgEMWTIEOzfvx+VlZXIzs7GunXrcPPNN8fk3ER9jcjL1zsECiCSU4BBQyEGDQ06Lms9wJ4y+Fa8CPnm/0GOOxMilVXIAXRafV/DZuHGpLUyywHafO0tkViFP6SIkrERI0Zg/fr1OPPMMzF27Fg8+OCDSEpKilmjcLPZjF//+teYM2cOfD4fzjrrLBQVFcXk3EREelAbz5tS0+B78PeQq96GOPdSvcOKD24nRPGwTp+mtERKY2kEo/FfoCHs2ZBtrcoxf3sz6lhEydhtt92m3Z42bRqKiorQ1NQUkyVK1cknn4yTTz45ZucjIooHYvBw4PjRkP95HfLs8xJ+dizi6vsqq40zYwajVd+3OSBam5Xl+ho3kF+gY1TxLepiRSaTCZMmTcKPfvQjpKby0m4ios6YLpim1Dz78C29Q9FfpNX3VVY7SyMYjVq6xJYdUJ7ErVc0hhDRzFhDQwPeeecdlJWVoampKeixe++9t0cCIyLqK8TgYcrs2AdvQJ59PkQiL9dEWH1fw7ZWxuN2AmkZECkpkP56f9Lj4kUYYUSUjD3yyCPw+Xw49dRTkZysYxFHIiKDMv10Gnxzbodc9RbEebFrJWc4kVbf9xNWO9taGYz0uNpnPjOzAGHiUnMnIkrGtm3bhkWLFsFisKKPRETxQhQPA04Y4987lrizYxFX31dl2dnWymjU6vuAUtMwy8pkrBMR7RkbMWIE9u7d29OxEBH1aaafTgMa6iA/XKF3KPrxuJRCvFn2yJ6vzqDVeHosJIoxtxMicOaT+/46FdFU14wZMzB37lwMHToUdrs96LFLLrmkJ+IiIupzxKChwKhT2/eOpWfoHVLvczsBqz3itlHCaleuxqt1A9nx3VKHlOr78DiDr5a1Ojgz1omIZsZefPFFVFdXw+PxYP/+/dq/AwcO9HR8RER9iukC/+zYqsScHVOq70e2XwwAC78aTWD1fT9htfP960REM2Pr1q3D448/Docjim8gIiI6ghg0BDjxNP/s2AWJNzsWafV9ldoSiVfjGUNg9X2V1Q54XGyJFEZEM2P9+/eHOcIpZSIiCs90wVSgoT4x9465nZGXtQCUJS6AMytGEVB9X2OzA95WoLFBn5gMIKKZsYkTJ+Khhx7Cj3/84yP2jB133HE9ERcRUZ8lBg4BThwLufINyMnnQ6Rn6h1Sr4i6+j4AkZKitERiMmYIgdX3NYFLzYk2ExyhiJKx999/H4CydyyQEAJPPvlk7KMiIurjTBdMhW/2Z5ArV0D8dJre4fQKn9sZXfV9lc3OZMwoAqvv+2kXYdS42BIphIiSsYULF/Z0HERECUUMLAFOGgu58k3I0gsSYnbM56oCgOCyB5FgaQTjCKi+r+FSc6ei7k1JRESxYbpgGtBYD7lsMeSubZAtzXqH1KPanEoyFvXMGK/GM4yg6vsq9SIMvochhZwZu/XWW/Hoo48CAK6//vqQJ3jqqadiHxURUQIQRYMhTp8MuXYl5NqVgMkE5Bcqs2ZFgyGKSoCBJRAZWXqHGhPqzFi0yZiwOtgSySgCqu9r1JZIHrcuIRlByGTsuuuu027fdNNNvRIMEVGiEb+6GeK8XwAVOyF374Ss2AX53f+Az1Yr+2wAIDtPSc4GlmgJGrLzDFcmwOesjq76vspqZ0sko3A7IYYdG3RImMyA1aYU7qUOhUzGRowYod32eDwYN27cEc/57LPPeiYqIqIEIYQA8vKBvHyIk8drx2WtR0nQKnYBu/2J2lcblQrnAJCe2T575k/UkF8IEcc9hNtcVVFV39doV+N5WIU/jnVYfV+VxX1/4UT0Xfv00093mIw988wzGDt2bMyDIiJKdCLLBhx7EsSxJ2nHZHMTsKdMSdDUmbQ17wKtLcosmiUJKBgUvMxZWAyRmqbb6wjkc1VFV33fT9jYEskQOqi+r/EXfqWOhU3GDh48CADw+XyorKxs/4vM/1hycnLPRkdERBqRkgoMGQExpH3lQra1AQf2QlbsBCp2QVbshNy8HvjkP0oCIwTQbwBE0WBl/5n6v7X3O6r4nFVR1RjTqMua/GUe3zqqvu8nbHbIA3t6OSDjCJuM3Xzzzdrtw/eN2e12XHrppT0TFRERRUSYzUDBQIiCgcDYMwH4l4tcVUpypi5x7voB+PzT9n1oNgdQpCRnykxaibJUauq5i+x9ziqIwsHRf2DA1XjG2iWXYDqqvq/yXxHLlkgdC5uMvfzyywCAWbNm4f777++VgIiIqHuEEMqm/+w8iFGnasdlQ502e4bd/lm0b7cos2uAUum+qDhgH9oQYMBAiKTub5qXXi98NW6IrsyMsU6VIXRYfV9ltftbItUr+x0pSER7xg5PxA4ePAghBPr169cjQRERUeyJ9Ezg6OMhjj5eOyZbW4F9uyF372hf5ly3CmhuVGbRzGbgqKKAZU5/ohbtL9Qad9eq78PfEimVLZHiXgfV9zVBLZGYjB0uomTssccew09+8hMcffTR+Oijj/Dss8/CZDLhqquuwtlnn93TMRIRUQ8RSUnAoCEQg4Zox6TPBxw6EFxu45stwPqP2pc5c/opyZmWoJUAjpzQS1D+/URRV99XsfBr/Ouo+r6fsDr8LZHcQH5hb0cW9yJKxr7++mvceOONAIC33noLM2fOREZGBubPn89kjIiojxEmE9B/ANB/AMToCdpx6XEFl9uo2AW55b/tF3dlZvn3oZW0XyzQv0DZ1+apVp7ThZkxAIDVwdIIca7D6vsqdd+fh/v+OhJRMub1emGxWOB0OlFXV6fVIPN4PD0aHBERxQ9hcwC2UyCOO0U7JpsagD3l7Vdz7t4JueotwNuqzIQkJwMFxUp3AaAbyZgd2Le7m6+AelRH1fdVgcuUdISIkrHi4mIsX74chw4dwsknnwwAcDqdSEuLj9o1RESkD5GaDgw9BmLoMdox6fUCB/YE1UNDxS6YcvKir76vfh6rHfK7r2IUNfWIDqrvazKzlIScyViHIkrGfvvb3+Lll1+G2WzGlVdeCQD44YcfMGHChE4+koiIEo2wWJRis4XFwLizACjlNnKzs1Ht6mKtMJsdaKiDbG2NydWdFFthq+/D3xIpywbUsFZcRyJKxvLz83HLLbcEHRs7diyr7xMRUUSEENG3QQqkLnPVupWyHRRfwlXfV7ElUkhhq/stXrw46P6qVauC7j/88MOxj4iIiOgwgnuO4luY6vsam53vXwhhk7E1a9YE3V+6dGnQ/f/973+xj4iIiOhwLPwa38JV3/cTLE8SUthkLLAXJRERkW600gjccxSPwlbfV1ntQI2LuUUHwiZj7B9FRERxgcuU8S1c9X2V1aHsK2us752YDCTsBv62tjZ8/fXX2n2fz3fEfSIiop4mklOAtHSgcj+ktxXCwisq40qY6vsatkQKKWwyZrPZ8NRTT2n3MzMzg+5brdaei4yIiChQbn/IdR9CblijVPofNBQoHgZRPAw4qkApn0C6CFt9309Y7UohYI+bLZEOEzYZW7hwYW/FQUREFJbp1gcgv/sfULYNsnw75GcfAavfUX7Bp6QBgw5L0PLyud2mt4Srvq/y7yeTNWyJdLiI6owRERHpTWTZIMZMAMYoBcelzwcc3Au5axtQvh2ybBvkR++0t2LKyAIGDYUoHgZR7E/SHGFKL1DXhau+r+K+v5B0T8aWLl2KTZs2wWKxoH///pgxYwYyMjL0DouIiOKcMJmAo4ogjioCxp8NwN+KaV85ZNk2oMyfoL33byVxA5QN5kXFEAWDgAJ/l4D8Qlb174bOqu9rMtSWSLwi9nC6J2MnnHACLrvsMpjNZvzzn//E8uXLccUVV+gdFhERGZCwWICBQyAGDgEmKcdkS7PSxFxN0PaWKX0uvV5lBs1kAvoXKIlZwSDtf+T04zJnJCKpvg9/8pxl58xYB3RPxkaNGqXdHj58OD777DMdoyEior5GJKcAQ0ZADBmhHZNeL1C5D3JvObCnXEnQdn4PbPwEWhWs1DQlOSsoBgoHtc+mZfBKwCCRVN9XWW1sidQB3ZOxQKtWrcL48eNDPr5y5UqsXLkSADBv3jzk5ub2VmghWSyWuIijL+BYxg7HMnY4lrETd2OZnw+ccHLQIV9DPby7d8JbvqP93+a1kB+/pyVpyaPGwD7zke712uymeBrL5j074AZgHzQYyZ3E5MrtB1+tBzlxErtK7/HslWRs9uzZcLvdRxyfOnUqxowZAwB47bXXYDabMXHixJDnKS0tRWlpqXa/qqoq5rFGKzc3Ny7i6As4lrHDsYwdjmXsGGYsc49S/p2iXCggpIRwO4E9ZZDffIGWlW/i0PtvwHTqJP1CjKOx9O0uAwB4YILoJCZfajpk+Y64iV3VG+M5YMCAkI/1SjI2c+bMsI+vXr0amzZtwn333cf1eSIiiitCCMCRo/wbeRLkN1sg33oZcvTprG0GRFZ9X2V1ADVuSCn5+z5A2HZIvWHLli144403cOeddyIlXOVeIiIinQmTCaYLpgL7KyA3fqp3OPHB7QTSO6m+r7Lalc3+DWyJFEj3PWOLFi2C1+vF7NmzAQDDhg3Dtddeq3NUREREIZw8HigYpMyOjZmQ8LNj0uOKbFYMCK41xgshNLonY3/961/1DoGIiChi6uyY7+m/QG78FOK0M/QOSV+RVN/3EzaHciFEjRs4ii2RVLovUxIRERnOSeP8s2MvQfra9I5GX24nhC18jTGNf2ZMsvBrECZjREREUVJmx6YBB/ZCbvhE73B0E3H1fRVbInWIyRgREVFXnDQWKCxW9o4l6uyYVn0/wmRMa4nk7tGwjIbJGBERURdoV1Ye3Au54WO9w9GHVn0/wj1jakskD5cpAzEZIyIi6qoT/bNjK16GbEvA2TF/jTER6cwYwJZIHWAyRkRE1EXa3rHKfQk5OyY9asHXCDfwq89lMhaEyRgREVF3nHgaUDhYubIy0WbHoqm+7yey7EzGDsNkjIiIqBuEyQTTT6cBlfsh/7tG73B6VzTV91VWO1CrtEQiBZMxIiKi7jrxNKBoMOTbibV3LKrq+yqbgy2RDsNkjIiIqJuEEAGzY6v1Dqf3RFF9X6PVGuMVlSomY0RERLEw6jRgYIlSd2z3Tkhvq94R9bxoqu/7CRZ+PYLuvSmJiIj6AiEETBdeCd9fH4Bv9u8AswUYUAQxsAQoKoEoKgGKBkOkpesdakxEXX1fZVWSN1njhuiBuIyIyRgREVGMiONPgWnOM5Bl24DdOyErdkJ+9Tmw9kNo29Xz8v3J2WAlURtYAtiyIYTBUpNoq++r1JkxFn7VMBkjIiKKIZGXD5GXD4yZCCBgBqlil7J8uXsnULETcvO69gQty6YlaBjon0XrfxSEyazb6+hUlNX3NRmZbIl0GCZjREREPUgIAdhzAHsOxPGjteOysUFJ0Cp2ARU7ICt2Qa58E2jzKklacgpQWOxf5hwMUTQEKBgIkRxFGYme1JXq+whoicRkTMNkjIiISAciLR0YPhJi+EjtmPS2Avv3QFbs9C9z7oL878fA6neVBM1kAvIL4Rk6Ar7+Be370DKtvR5/l6rvq2x2tkQKwGSMiIgoTghLkn8WbDAwfjIA/zJn1UFladO/1Nmy9QvIj//TvsyZnatdJKAudSKnX8/uQ+tC9X2N1c6ZsQBMxoiIiOKYEELZ9J+XD3HyeABAbm4uDu3a4V/mDJhF++pzSOlTPjA9o30fWlEJxMDBQH4RhCVGv/q7Un1ffU1Zdsi9u2MTRx/AZIyIiMiARJYNOPZEiGNP1I7J5mZgb5n/IgElUZNr3gNaW5RZNEsSUDAo4EKBwUDhYIjUtKg/f5eq76v8zcKllMa7irQHMBkjIiLqI0RKClByNETJ0dox2dYGHNyrXCigltvY8hnw6QdKgiYEkHdU+4UCal20zvaCdaX6vspqB9q8QEMdkJHVtXP0IUzGiIiI+jBhNgMDBkIMGAicdgYA/z40V7V/9sx/JWfZNuDzT9v3odkc/v1r/uRsYImyVGryN+9xOyGGHdu1oAKr8DMZYzJGRESUaIQQyqb/7FyIUWO047KhDqgoC96H9u1yoK1NSdJS0oCiYiVB60r1ffXzW+3K+WrcwFFFMXhFxsZkjIiIiAAAIj0TOPo4iKOP047J1lZg3+7gBG3dKqX6flcTKbUlksfFlkhgMkZERERhiKQkYNAQiEFDtGPS5wNqXFpSFTU2Cw/CZIyIiIiiIkwmpatAV2VkAmYzkzE/k94BEBERUWJRWiLZlNk1YjJGREREOrDaIWs8ekcRF5iMERERUe+zOrhM6cdkjIiIiHqdsNoBD5cpASZjREREpAerHaj1QHq9ekeiO15NSURERL0vOw9o88J381SlT+agoUDxMIjiYUD/Ae2V/hMAkzEiIiLqdWLCOUBmFrDrB8iybZBrVwKr3lIq86elAwOHQBQPgxg8DBg0FMjp12ebijMZIyIiol4nkpIgxkwExkwEAEhfG7B/D2TZdqBsm5Kgffhm+zJmlg0YNFRJ0Ir9s2idNTM3CCZjREREpDthMgMFgyAKBgGnTwYASG8rsKcsOEHb+gWk9Ckf5MhVljgLioFC/8f2L1CaoxsIkzEiIiKKS8KS1L6PDD8BAMjmJqVHZtk2JUGr2AX5v88Bn09Z4rQkAUcVBiRoyv+wZcftMmfcJGMrVqzA0qVL8eyzz8JqteodDhEREcUhkZIKDDsWYtix2jHZ2grsr4DcWw7sLYPcWw753ZfAZx8pCRoAZGQpM2+Fxe0zcAWDIFLT9HgZQeIiGauqqsJXX32F3NxcvUMhIiIigxFJScpy5cCSoOOyvhbYUw65t0xZ7txbDrn2Q6C5sT1Jy8tH87W3A8VH93bYmrhIxp5//nlcfvnlmD9/vt6hEBERUR8hMrKAo4+DOPo47Zj0+YDqSmUGbU85sLccJp0vBNA9Gdu4cSOys7NRXFzc6XNXrlyJlStXAgDmzZsXFzNpFoslLuLoCziWscOxjB2OZexwLGOHY9lN/foBx7QnaBaLBUk6Fp/tlWRs9uzZcLvdRxyfOnUqli9fjnvvvTei85SWlqK0tFS7X1VVFasQuyw3Nzcu4ugLOJaxw7GMHY5l7HAsY4djGVu9MZ4DBgwI+VivJGMzZ87s8Pju3btRWVmJO+64AwBQXV2NO++8E3PnzoXdbu+N0IiIiIh0pesy5cCBA/Hss89q92+44QbMnTuXV1MSERFRwkicxk9EREREcUj3DfyBFi5cqHcIRERERL2KM2NEREREOmIyRkRERKQjJmNEREREOhJSStn504iIiIioJ3BmrJvuuusuvUPoMziWscOxjB2OZexwLGOHYxlbeo8nkzEiIiIiHTEZIyIiItIRk7FuCuyVSd3DsYwdjmXscCxjh2MZOxzL2NJ7PLmBn4iIiEhHnBkjIiIi0hGTMSIiIiIdxVVvSqN46aWX8Pnnn0MIAZvNhhkzZiA7OxtSSixZsgRffPEFUlJSMGPGDJSUlOgdblxbunQpNm3aBIvFgv79+2PGjBnIyMgAACxfvhyrVq2CyWTCVVddhRNPPFHfYA1g/fr1eOWVV7B37148+OCDGDJkiPYYxzN6W7ZswZIlS+Dz+TB58mRceOGFeodkGH/729+wefNm2Gw2LFiwAABQV1eHRx99FIcOHUJeXh5uvfVWZGZm6hxp/KuqqsLChQvhdrshhEBpaSnOPfdcjmcXtLS0YNasWfB6vWhra8PYsWMxZcoUVFZW4rHHHkNtbS1KSkpw0003wWLpxRRJUtTq6+u122+//bZ85plnpJRSbtq0Sc6ZM0f6fD75/fffy7vvvluvEA1jy5Yt0uv1SimlXLp0qVy6dKmUUsqKigr5+9//Xra0tMiDBw/KG2+8Uba1tekZqiFUVFTIvXv3ylmzZsnt27cHHed4RqetrU3eeOON8sCBA7K1tVX+/ve/lxUVFXqHZRhbt26VO3bskLfddpt2bOnSpXL58uVSSimXL1+ufb9TeE6nU+7YsUNKKWVDQ4O8+eabZUVFBcezC3w+n2xsbJRSStna2irvvvtu+f3338sFCxbITz/9VEop5TPPPCPff//9Xo2Ly5RdkJ6ert1ubm6GEAIA8Pnnn2PSpEkQQmD48OGor6+Hy+XSK0xDGDVqFMxmMwBg+PDhcDqdAICNGzdi/PjxSEpKQr9+/ZCfn4/t27frGaohFBYWYsCAAUcc53hGb/v27cjPz0f//v1hsVgwfvx4bNy4Ue+wDOPYY489YpZm48aNOOOMMwAAZ5xxBsczQg6HQ1tlSUtLQ0FBAZxOJ8ezC4QQSE1NBQC0tbWhra0NQghs3boVY8eOBQCceeaZvT6WXKbsohdffBEff/wx0tPTMWvWLACA0+lEbm6u9pycnBw4nU44HA69wjSUVatWYfz48QCUsRw2bJj2WHZ2tpaoUfQ4ntFzOp3IycnR7ufk5GDbtm06RmR8Ho9H+3lot9vh8Xh0jsh4KisrsWvXLgwdOpTj2UU+nw933nknDhw4gP/3//4f+vfvj/T0dG1iQI+fj0zGQpg9ezbcbvcRx6dOnYoxY8Zg2rRpmDZtGpYvX4733nsPU6ZM6f0gDaKzsQSA1157DWazGRMnTuzl6IwnkvEkindCCG1VgSLT1NSEBQsWYPr06UErNADHMxomkwnz589HfX09Hn74Yezbt0/vkJiMhTJz5syInjdx4kTMnTsXU6ZMQXZ2NqqqqrTHqqurkZ2d3VMhGkZnY7l69Wps2rQJ9913n/bDJDs7G9XV1dpznE4nx9Iv0q/NQBzP6B0+Zvx+7j6bzQaXywWHwwGXywWr1ap3SIbh9XqxYMECTJw4EaeddhoAjmd3ZWRkYOTIkfjhhx/Q0NCAtrY2mM1mXX4+cs9YF+zfv1+7vXHjRm2PzujRo/Hxxx9DSokffvgB6enpXKLsxJYtW/DGG2/gzjvvREpKinZ89OjRWLduHVpbW1FZWYn9+/dj6NChOkZqbBzP6A0ZMgT79+9HZWUlvF4v1q1bh9GjR+sdlqGNHj0aa9asAQCsWbOGM7kRklLi6aefRkFBAc4//3ztOMczejU1NaivrwegXFn51VdfoaCgACNHjsRnn30GQJkg6O3vdVbg74KHH34Y+/fvhxACubm5uPbaa7XSFosWLcKXX36J5ORkzJgxI6i0AB3ppptugtfr1Tb6Dhs2DNdeey0AZenyo48+gslkwvTp03HSSSfpGaohbNiwAYsXL0ZNTQ0yMjJQXFyMe+65BwDHsys2b96M559/Hj6fD2eddRYuvvhivUMyjMceewzffPMNamtrYbPZMGXKFIwZMwaPPvooqqqqWIohCt999x3uu+8+DBw4UFs9mDZtGoYNG8bxjFJ5eTkWLlwIn88HKSXGjRuHSy65BAcPHsRjjz2Guro6DB48GDfddBOSkpJ6LS4mY0REREQ64jIlERERkY6YjBERERHpiMkYERERkY6YjBERERHpiMkYERERkY6YjBEliNtuuw1bt27VO4ywVq9e3aWitqoHH3wQq1evjl1ARES9gBX4ifqIK6+8Urvd0tICi8UCk0n5e+vaa6/FI4880itxLFu2DMuXL4fFYoHZbEZhYSF++ctfYvjw4TH/PAcOHMDNN9+sHfvjH/8Y08+hmjJlCp544gnk5+dH9XHffvstHnzwQe1+c3NzUHHjRx99NKifbXfccMMNuO6663DCCSfE5HzRWL16NT788EPMnj271z83UV/AZIyoj1i6dKl2W89fzAAwbtw43HzzzWhra8OyZcvwyCOP4Omnn9YlFj0dc8wx2vtSWVmJG2+8Ec8995zWkDiQ2oqFiBIPkzGiBBGYoC1btgx79uyBxWLB559/jry8PNx+++3473//i7fffhtJSUn47W9/i1GjRgEAGhoa8Pzzz+OLL76AEAJnnXUWpkyZos28haI2f1++fDlqampgtVqjOteSJUuwYcMGNDQ0ID8/H9OnT8cxxxyDLVu2YPny5QCUlmT5+fmYP38+/vSnP2HixImYNGkSrrnmGjzwwAMYOHAgAKUNyvXXX4+//e1vsNls2LRpE1566SUcOnQIhYWFuOaaazBo0KAjYpg1axYA4I477gAAXH/99Rg/fjxWrlyJN954A3V1dRgxYgSuueaaqPrZLVu2DBUVFUhKSsKmTZvwy1/+EuPGjQs5NgcOHMAzzzyD8vJyCCEwatQoXH311cjIyMBf//pXVFVV4S9/+QtMJhMuueQSjBs3DjfeeCOuv/56LFu2DE1NTZg2bRpKSkrw9NNPo6qqChMnTsTVV1+txbRq1SqsWLECbrcbQ4cOxbXXXou8vDwAyuzgb37zG7z11luoqanBhAkTcPXVV2Pv3r34xz/+Aa/XiyuvvBJmsxnPPfdcxONARNwzRpSwNm3ahEmTJmHJkiUYPHgw5syZo/XA+/nPf46///3v2nMXLlwIs9mMJ554Ag899BC+/PJLfPjhh51+Dq/XizVr1iArKwsZGRlRn2vIkCF46KGHsHjxYkyYMAGPPPIIWlpacOKJJ+Kiiy7CuHHjsHTpUsyfPz/o45KSknDqqadi7dq12rF169bh2GOPhc1mw65du/DUU0/h2muvxeLFi1FaWoqHHnoIra2tR8Rw//33AwDmz5+PpUuXYvz48fj666/x4osv4tZbb8Xf//535OXl4fHHH+980A/z+eefY+zYsViyZAkmTpzY6dhcdNFFeOaZZ/Doo4+iuroar7zyCgClrVhubi7uvPNOLF26FD/72c+0j9m2bRsef/xx/O53v8Pzzz+P1157DTNnzsQjjzyC9evX45tvvgGgJLXLly/H7bffjmeffRYjRow44jVt3rwZc+fOxcMPP4z169fjyy+/1BLZ4cOHY+nSpUzEiLqAyRhRghoxYgROPPFEmM1mjB07FjU1NbjwwgthsVhw+umn49ChQ6ivr4fb7cYXX3yB6dOnIzU1FTabDeeddx7WrVsX8tzr16/H9OnTcfnll+PDDz/EbbfdBrPZHPW5Jk2ahKysLJjNZlxwwQXwer3Yt29fRK9vwoQJQeddu3YtJkyYAABYuXIlSktLMWzYMJhMJpx55pmwWCzYtm1bROf+5JNPcNZZZ6GkpARJSUm47LLL8MMPP6CysjKij1cNHz4cp556KkwmExoaGsKOTX5+Pk444QQkJSXBarXivPPO0xKpcC655BIkJydj1KhRSElJwYQJE2Cz2ZCdnY0RI0Zg165dAIAPPvgAF110EQoLC2E2m3HRRRehrKwMhw4d0s514YUXIiMjA7m5uRg5ciTKysqier1E1DEuUxIlKJvNpt1OTk6G1WrVlgqTk5MBAE1NTXC5XGhra9MauAOAlBI5OTkhz63uGaupqcGCBQuwc+dOjBw5ElVVVVGd680338RHH30Ep9MJIQQaGxtRW1sb0es77rjj0NzcjG3btsFms6GsrAynnnoqAKCqqgpr1qzBe++9pz3f6/XC6XRGdG6Xy4XBgwdr91NTU5GZmQmn04l+/fpFdA4AQa+7s7Fxu9147rnn8O2336KpqQk+ny+iptCHv8+H329qagIAHDp0CEuWLMELL7wQ9PmdTqe2VGm327XHUlJStI8lou5hMkZEYeXk5MBisWDRokVRbzC3Wq247rrrcNddd2HChAlRnevbb7/Fm2++ifvuuw+FhYUwmUy46qqrIKUEAAghwn68yWTCuHHjsHbtWthsNpx88slIS0vTXtPFF1+Miy++OKrXo3I4HKiqqtLuNzU1oa6uLqo9Y4frbGxefPFFAMCCBQuQmZmJDRs2YPHixV3+fIfLzc3FxRdfjIkTJ8bsnEQUGS5TElFYDocDo0aNwgsvvICGhgb4fD4cOHAgoiUyABgwYABGjRqFN954I6pzNTY2wmw2w2q1wufz4d///jcaGhq0x202Gw4dOgSfzxfyc6tLlZ9++qm2RAkAkydPxgcffIBt27ZBSommpiZs3rwZjY2NHZ7HZrPh4MGD2v3TTz8dH330EcrKytDa2ooXX3wRQ4cOjWpW7HCdjU1jYyNSU1ORnp4Op9OJFStWBH283W6Pepk00DnnnIPXX38dFRUVAJSLNtavXx/Rx9rtdjidTni93i5/fqJExpkxIurUjTfeiP/7v//DbbfdhsbGRvTv3z9ok3hnfvrTn+KBBx7ARRddFPG5TjzxRIwaNQq33HILUlJScN555wXV5Bo3bhw++eQTXH311ejXrx/+8pe/HHGOYcOGISUlBU6nEyeddJJ2fMiQIbjuuuuwePFi7N+/H8nJyRgxYgSOOeaYDuO/9NJLsXDhQrS0tODaa6/F+PHj8Ytf/AILFixAXV0djj76aPzud7+LeDxCCTc2l156KZ588kn86le/Qn5+PiZNmoS3335b+9gLL7wQixcvxj//+U9cfPHFGDt2bFSf+9RTT0VTUxMee+wxVFVVIT09HccffzzGjRvX6cced9xx2kZ+k8mERYsWRffCiRKckOqcPxERERH1Oi5TEhEREemIyRgRERGRjpiMEREREemIyRgRERGRjpiMEREREemIyRgRERGRjpiMEREREemIyRgRERGRjv4/Df27iVYbO+QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "effects = (twfe_model.params[twfe_model.params.index.str.contains(\"relative_days\")]\n",
    "           .reset_index()\n",
    "           .rename(columns={0:\"effect\"})\n",
    "           .assign(relative_day=lambda d: d[\"index\"].str.extract(r'\\[(.*)\\]').astype(int))\n",
    "           # set the baseline to period -1\n",
    "           .assign(effect = lambda d: d[\"effect\"] - d.query(\"relative_day==-1\")[\"effect\"].iloc[0]))\n",
    "\n",
    "# effects\n",
    "effects.plot(x=\"relative_day\", y=\"effect\", figsize=(10,4))\n",
    "plt.ylabel(\"Estimated Effect\")\n",
    "plt.xlabel(\"Time Relative to Treatment\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4211d580",
   "metadata": {},
   "source": [
    "We can kind of see it is a bit better because at least the estimated effect after the treatment is 1) mostly negative and 2) mostly decreasing. But there are these weird spikes and what looks like a positive pre-treatment effect, which obviously doesn’t make any sense.\n",
    " \n",
    "The problem here is the same we’ve been discussing. Since we have different timing in the treatment, the early treated gets used as a control for the late treated units, which causes the model to estimate a very weird counterfactual trend. The bottom line is that adding time relative to treatment dummies does not solve the problem, but what does, exactly?\n",
    " \n",
    "## 3) Enlightenment: A Flexible Functional Form\n",
    " \n",
    "There are good news and bad news. First, the good news: we've identified the problem as being one related to the functional form, so we can fix it by correcting that form. Namely, we've said over and over that this specific bias in TWFE comes from the time heterogeneous effects. This can happen, among many other reasons, because the effect takes some time to mature (ex: it might take 10 days for a marketing campaign to reach its full potential). In other words, the functional form of traditional TWFE is simply not flexible enough to capture this heterogeneity, leading to the sort of bias we've discussed. Like in most cases, knowing the problem is already a long way in the direction of finding a solution. \n",
    " \n",
    "In the end of the last section, we saw how simply allowing for a different effect on each time period relative to the treatment (event study design) was not enough. Even though that didn’t work, the intention behind it was good. It did make the model more flexible, but not in a way it solved the problem. We need to think of another way to make the model even more flexible than that.\n",
    " \n",
    "To do that, let's go back to our original example, when we were trying to model the number of incremental installs that rolling out a new feature (treatment) brought us. We saw that the simple TWFE model does not work here:\n",
    " \n",
    "$$\n",
    "Installs_{it} = \\tau D_{it} + \\gamma_i + \\theta_t + e_{it}\n",
    "$$\n",
    " \n",
    "More than that, we know it doesn't work because it is too restrictive. It forces the effect to be the same \\\\(\\tau_{it}=\\tau \\ \\forall i, t\\\\), that is, it forces time homogeneity. If that is the problem, an easy fix would be to simply allow for a different effect for each time and unit. \n",
    " \n",
    "$$\n",
    "Installs_{it} = \\sum_{i=0}^N \\sum_{t=0}^T \\tau_{it} D_{it} + \\gamma_i + \\theta_t + e_{it}\n",
    "$$\n",
    " \n",
    "This would be equivalent to running the formula below:\n",
    "```\n",
    "installs ~ treat:C(unit):C(date) + C(unit) + C(date)\n",
    "```\n",
    " \n",
    "Unfortunately, we can't fit that. It would have more parameters than we have data points. Since we are interacting date and unit, we would have one treatment effect parameter for each unit for each time period $T*N$. But this is exactly the number of samples we have! OLS wouldn't even run here.\n",
    " \n",
    "Ok, now, we need to reduce the number of treatment effect parameters of the model. To achieve that, we can think about a way of somehow grouping units. If we think about this a bit, we can see a very natural way to group units: by cohort! We know that the effect in an entire cohort follows the same pattern over time. So, a natural improvement on that impractical model above is to allow the effect to change by cohort instead of units:\n",
    " \n",
    "$$\n",
    "Installs_{it} = \\sum_{g=0}^G \\sum_{t=0}^T \\tau_{gt} D_{it} + \\gamma_i + \\theta_t + e_{it}\n",
    "$$\n",
    " \n",
    "where `G` is the total number of cohorts and `g` marks each individual cohort. That model has a much reasonable number of treatment effect parameters ($T*G$) since $G$ is usually much smaller than $N$. Now, we can finally run it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c1183755",
   "metadata": {},
   "outputs": [],
   "source": [
    "formula = f\"\"\"installs ~ treat:C(cohort):C(date) + C(unit) + C(date)\"\"\"\n",
    "\n",
    "# for nicer plots latter on\n",
    "df_heter_str = df_heter.astype({\"cohort\": str, \"date\":str})\n",
    "\n",
    "twfe_model = smf.ols(formula, data=df_heter_str).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9ecfc5c",
   "metadata": {},
   "source": [
    "To see if this model works, we can make counterfactual predictions for $Y_0$ by forcing `treat` to be zero for everyone. Then, we can estimate the effect by taking the observed outcome for the treatment, which is $Y_1$, and subtract $\\hat{Y}_0$ from it. Let's see if that matches the true ATT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "579f181d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of param.: 467\n",
      "True Effect:  0.8544117647058823\n",
      "Pred. Effect:  0.8544117647058977\n"
     ]
    }
   ],
   "source": [
    "df_pred = (df_heter_str\n",
    "           .assign(**{\"installs_hat_0\": twfe_model.predict(df_heter_str.assign(**{\"treat\":0}))})\n",
    "           .assign(**{\"effect_hat\": lambda d: d[\"installs\"] - d[\"installs_hat_0\"]}))\n",
    "\n",
    "print(\"Number of param.:\", len(twfe_model.params))\n",
    "print(\"True Effect: \", df_pred.query(\"treat==1\")[\"tau\"].mean())\n",
    "print(\"Pred. Effect: \", df_pred.query(\"treat==1\")[\"effect_hat\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7000d91c",
   "metadata": {},
   "source": [
    "It does! We finally managed to make a model which is flexible enough to capture the time heterogeneity, which allowed us to estimate the correct treatment effect! Another cool thing we can do is extract the estimated effects by time and cohort and plot them. In this case, because we know how the data was generated, we know what to expect. Namely, the effect for each cohort must be zero before treatment, then 1 10 days after treatment and a line climbing up from zero to 1 in the days between the treatment and 10 days after it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "630c7699",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "effects = (twfe_model.params[twfe_model.params.index.str.contains(\"treat\")]\n",
    "           .reset_index()\n",
    "           .rename(columns={0:\"param\"})\n",
    "           .assign(cohort=lambda d: d[\"index\"].str.extract(r'C\\(cohort\\)\\[(.*)\\]:'))\n",
    "           .assign(date=lambda d: d[\"index\"].str.extract(r':C\\(date\\)\\[(.*)\\]'))\n",
    "           .assign(date=lambda d: pd.to_datetime(d[\"date\"]), cohort=lambda d: pd.to_datetime(d[\"cohort\"])))\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "sns.lineplot(data=effects, x=\"date\", y=\"param\", hue=\"cohort\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel(\"Estimated Effect\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5479dd42",
   "metadata": {},
   "source": [
    "Once again, the plot above matches our expectations of the effects. They follow the exact pattern we described above.\n",
    " \n",
    "This is already very good, but we can do even better. First, notice how that model has a huge number of parameters. Since we have 100 units and about 92 days in our data, we know that 192 of those parameters are the unit and time effects. Still, that leaves us with more than 250 treatment effect parameters. \n",
    " \n",
    "If we assume a zero effect before the treatment (no anticipation), we can reduce the number of parameters by dropping the dates before treatment from the interaction term. \n",
    " \n",
    "$$\n",
    "Installs_{it} = \\sum_{g=0}^G \\sum_{t=g}^T \\tau_{gt} D_{it} + \\gamma_i + \\theta_t + e_{it}\n",
    "$$\n",
    " \n",
    "Also, we can drop from the interaction the control cohorts, since its effect before the treatment is always zero\n",
    " \n",
    "$$\n",
    "Installs_{it} = \\sum_{G=q}^g \\sum_{t=g}^T \\tau_{gt} D_{it} + \\gamma_i + \\theta_t + e_{it}\n",
    "$$\n",
    " \n",
    "where cohorts before $g<q$ are defined as the control cohorts. \n",
    " \n",
    "Notice however that this is tricky to implement with formulas, so we have to do some feature engineering first. Namely, we will create the cohort dummies by hand, making one column which is 1 when the cohort is `2021-06-01` and 0 otherwise and another column which is 1 when the cohort is `2021-07-15` and 0 otherwise. Also, we will create a date column for cohort `2021-06-01` which collapses all dates before that cohort date to a `control` category. We can do a similar thing with the dates from the `2021-07-15` cohort. Here is what this looks like in code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2f2a9bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_eng(df):\n",
    "    return (\n",
    "        df\n",
    "        .assign(date_0601 = np.where(df[\"date\"]>=\"2021-06-01\", df[\"date\"], \"control\"),\n",
    "                date_0715 = np.where(df[\"date\"]>=\"2021-07-15\", df[\"date\"], \"control\"),)\n",
    "        .assign(cohort_0601 = (df[\"cohort\"]==\"2021-06-01\").astype(float),\n",
    "                cohort_0715 = (df[\"cohort\"]==\"2021-07-15\").astype(float))\n",
    "    )\n",
    "\n",
    "formula = f\"\"\"installs ~ treat:cohort_0601:C(date_0601) \n",
    "                       + treat:cohort_0715:C(date_0715) \n",
    "                       + C(unit) + C(date)\"\"\"\n",
    "\n",
    "twfe_model = smf.ols(formula, data=df_heter_str.pipe(feature_eng)).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef57dc71",
   "metadata": {},
   "source": [
    "If we now make counterfactual predictions like before, we can see that the estimated effect is still matching the true effect perfectly. The gain here is that we have a much simpler model, with only about 80 treatment effect parameters (remember that 192 of those parameters are the time and unit fixed effect)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "58193064",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "271\n",
      "True Effect:  0.8544117647058823\n",
      "Pred Effect:  0.8544117647059067\n"
     ]
    }
   ],
   "source": [
    "df_pred = (df_heter\n",
    "           .assign(**{\"installs_hat_0\": twfe_model.predict(df_heter_str\n",
    "                                                           .pipe(feature_eng)\n",
    "                                                           .assign(**{\"treat\":0}))})\n",
    "           .assign(**{\"effect_hat\": lambda d: d[\"installs\"] - d[\"installs_hat_0\"]}))\n",
    "\n",
    "\n",
    "print(len(twfe_model.params))\n",
    "print(\"True Effect: \", df_pred.query(\"treat==1\")[\"tau\"].mean())\n",
    "print(\"Pred Effect: \", df_pred.query(\"treat==1\")[\"effect_hat\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b64c22ce",
   "metadata": {},
   "source": [
    "Plotting the treatment effect parameters, we can see how we've removed those from the control cohort and those from the dates before the cohort is treated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "baf0bb75",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAAEvCAYAAAAabYYDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABFFklEQVR4nO3deXhTZdo/8O9JQlu6N91ZC4VWFllKZQcLLTsIzqiI4ziCsyiOiPqqP1RGfQFlFvWdcRkdRMYRdBhUFMsmpQKyylJ26AItWLrRJN2btMk5vz8qGSsUQrOcnJzv57q4LpKcJPf9pE969znn3EeQJEkCEREREXkljdwBEBEREVHbWKwREREReTEWa0RERERejMUaERERkRdjsUZERETkxVisEREREXkxndwBOKukpMStrx8VFYXKykq3voc3UVO+zJXaoqbxYq6+S235upsrxrNTp07teh5X1oiIiIi8GIs1IiIiIi/GYo2IiIjIiyn+mLWfkiQJZrMZoihCEASnX6+8vBwWi8UFkbmeJEnQaDQICAhwSa5ERETkfXyuWDObzejQoQN0OtekptPpoNVqXfJa7mC1WmE2m9GxY0e5QyEiIiI38Eix9s477+DIkSMICwvDa6+9dtXjkiRh1apVyMnJgb+/P+bPn4+ePXu2671EUXRZoaYEOp3Oa1f+iIiIyHkeOWYtLS0Nzz33XJuP5+TkoKysDH/729/w29/+Fu+//36730uNuwPVmDMREZFaeGQJqm/fvqioqGjz8UOHDmHs2LEQBAFJSUmor6+HyWRCRESEJ8IjD5KO7IN0OkfuMAAANQEdIZob5Q7DI9SUqyuoabyYq+9yd777EYWj0Lvt9T1teGI0UkYMlDuMa/KK/YVGoxFRUVH225GRkTAajdcs1rKyspCVlQUAWL58eavnAS0nBLh6N+i1Xm/BggWYMGECZsyY0a7XrK6uxueff465c+c6Gx78/f2vGof20ul0LnutnxIbG1D54ZuAaIPgH+CW97gZFkGAIElyh+ERasrVFdQ0XszVd7kz30ZNB7zZ7/cQBQ38xWa3vIen9aq1XPf3nzt/P96IVxRrNyMjIwMZGRn22z/tJmyxWFx6QoBOp4PVar3qflEUYbPZrvnYjVitVhiNRqxatQq//OUvnY7RYrG4rEu1Ozteizs2QWqog+bZP0Lo1cct73Ez1NTdW025uoKaxou5+i535rszz4SGg+VYPrEb+kQHuuU95HC98VL9FQz0en2rATAYDNDr5VlaXbdunb0gfOyxx3Dx4kXcfffdyMjIwD333INLly7Ztz1w4ADuuOMOjBgxApmZmQBaTpZYsmQJxo8fj/T0dHz55ZcAgL179+LOO+/Egw8+iLS0NLzyyiu4cOECJkyYgCVLlsiSqydJkgQpeyPQLRFIvEXucIiIqJ0kSUJmrgmJen/cEsVOBJ7gFStrqamp2LJlC0aNGoX8/HwEBgbKcrxabm4u/vrXv2LDhg3Q6/UwmUx44okncPfdd+Oee+7Bv//9byxevBgffPABgJZdrl988QUKCgowd+5cTJ8+HZs2bcKpU6ewbds2GI1GTJ06FcOHDwcAnDhxAtnZ2ejWrRu+//575ObmYtu2bR7PUxZnjwOl30OY+zhPiCAiUrBjZQ0ormnC4yPi+X3uIR4p1v7v//4Pp0+fRm1tLR5++GHcc8899t2HEydOxODBg3HkyBEsWLAAfn5+mD9/vifCusqePXswffp0+6peREQEDh8+jBUrVgAAfv7zn2Pp0qX27SdPngyNRoOkpCRcvnwZAPDdd99h1qxZ0Gq1iI6OxvDhw3Hs2DEEBwdj0KBB6Natm+cT8wLi9q+A4FAIt42ROxQiInLCxjwTQv21GN09RO5QVMMjxdrChQuv+7ggCPj1r3/tiVBcys/Pz/5/yYGDOAMDfWe//s2QLpcBxw9CmHI3hA5+N34CERF5pfK6JhwsrsNd/SLhp/WKI6lUgSP9I6NGjUJmZiaMRiMAwGQyITU11X7c2eeff45hw4Zd9zWGDRuGDRs2wGazwWAw4MCBAxg0aNBV2wUFBaGurs7lOXgjaccmQBAg3D5Z7lCIiMgJm/KqIAjAlKRwuUNRFa84Zs1bJCcnY8GCBbjrrrug0WjQv39/vPLKK1iwYAHeffdd6PV6vPHGG9d9jSlTpuDw4cOYMGECBEHA888/j5iYGBQUFLTaTq/X47bbbsP48eMxbtw4LF682J2pyUaymCHt3gYhZSQEvTynPBMRkfPMVhHbzlVhRNcQRAZ2kDscVREkR/bfebGSkpJWtxsaGly6u7Gt1h3exJU5u/pUb3HnFkir34HmmeUQevd12eu6gppO41dTrq6gpvFirr7L1fluyTfh79+VY/mEbugTo77DelTfuoN8U0u7jkygW0/AC/qqERFR+0iShI25JvSM8Mct0WzX4Wks1sh9zh4HSi5CGD+dp3cTESnYifIGXKxuwvTkCH6fy4DFGrmNmL2xpV3H0LFyh0JERE7IzG1p1zEmIVTuUFSJxRq5hVRZDhz7DsKYiWzXQUSkYOV1TTh4qQ4Te4WzXYdMOOrkFi3tOgAhbYrcoRARkRM251UBYLsOObFYI5eTLBZI324DBg+HoI+WOxwiImony4/adUSxXYdsWKy5yTfffIMxY8Zg1KhReOuttwC0nE2zfPlyjB49GrfffjtWrlxpv3/x4sUYNWoUMjIycOLECfvr/OIXv0CfPn3wwAMPyJJHe0gHdgANddCMnyF3KERE5ISdRTWoaxIxLdnz1+um/2JTXDew2Wx4/vnn8cknnyA+Ph5Tp07FxIkTkZOTg5KSEuzatQsajcberyU7OxuFhYXYvXs3jhw5gkWLFiEzMxMA8PDDD6OxsRGrV6+WMyWH2dt1dOkBeFlfNSIicpwkScg8a0KPCH/0ZbsOWXFlzQ1ycnKQkJCA7t27w8/PDzNnzsTWrVvxr3/9C0888QQ0mpZhj4pq6ei/detW3HXXXRAEAUOGDEF1dTXKy8sBAGPGjEFwcLBsudy0vJPApQsQ0tmug4hIyU5WNOBCtYXtOryAT6+sif9eAen7QudeQxBaXaRd6NoDmnt/c93nlJWVtepSHB8fj5ycHBQVFWHDhg3YsmULIiMj8b//+7/o2bPnNbcvKytDbGysU7HLQdz+FRAcwnYdREQKl5lrQoi/FmO6s12H3Liy5kFNTU3w9/fH5s2bcd999+Gpp56SOySXkgwVwNEf2nX4+csdDhERtVNFXTO+K67DxMQw+OtYKsjNp1fWbrQC5oj2XBs0Li6u1TVLS0tLERcXZz9+DWi54PuTTz553e2VRvpmEwBAuH2qzJEQEZEzNuebAABTknhigTdguewGgwYNQmFhIS5evIimpiZ8+eWXmDhxIiZPnoy9e/cCAPbt24eePXsCACZOnIhPP/0UkiTh8OHDCA0NVdwuUMligbT7h3YdkWzXQUSkVBariG0FVRjWJQTRQWzX4Q18emVNLjqdDkuXLsV9990HURQxe/ZsJCcn49FHH8Xvf/97rFixAoGBgfjzn/8MAEhPT0d2djZGjRqFjh074vXXX7e/1p133omCggI0NDRgyJAheO2115CWliZTZm2TvtsJ1NdCM3663KEQEZETdhbVoLZJxAy26/AaLNbcJD09Henp6a3uCwsLw0cffXTVtoIg4JVXXrnm66xfv94t8bnSf9t1JABJ/eQOh4iI2kmSJGzMNSEh3B99Y9iuw1twNyg5L/8UUFwEYTzbdRARKdmpikYUVbFdh7dhsUZOE7dnAkEhEIbeLncoRETkhMxcE0L8NBibwHYd3oTFGjlFMlwGju6HMHoCBH+26yAiUqrL9c04UFyLCb3C2a7Dy/DTIKdIOzcBEiCMY7sOIiIl25z3Q7uO3jyxwNuwWKN2k5oskL79Ghg0FEJkjNzhEBFRO1msIr4+V41hXYIRE8x2Hd6GxRq1m/TdLqCuFpr0GXKHQkRETvj2Qg1qLTZMY7sOr8RizU2++eYbjBkzBqNGjcJbb70FoKVn2oQJEzBhwgSkpKRg3rx5AICCggLMmDEDPXr0wLvvvtvqdYYNG4b09HRMmDABU6ZM8XgebbG36+jcHUjqL3c4RETUTpIkITPXhO7h/ugfEyh3OHQN7LPmBjabDc8//zw++eQT+yWmJk6c2Kpn2m9+8xtMnDgRABAeHo4lS5Zgy5Yt13y9devWQa/XeyR2h+WfBr4vhPDLR3l6NxGRgp2+3IhCkwWPDovj97mX4sqaG+Tk5CAhIQHdu3eHn58fZs6cia1bt9ofr62txZ49ezB58mQAQFRUFAYNGoQOHZRznICUnQkEBkMYliZ3KERE5ISNuSYE+2lwO9t1eC2fXll7/1A5Ck1mp15DEARIkmS/3SMiAL9Ovf51O8vKytCpUyf77fj4eOTk5Nhvb9myBaNGjUJISIhD7z9nzhwIgoD7778f999/fzuycC3JeBlSzj4IE2ayXQcRkYJdrm/Gvu9rMfMWPdt1eDGfLta81Zdffok5c+Y4tO369esRHx+PyspK3HvvvejVqxeGDx/u5givT9q5paVdRxrbdRARKdmW/CoAwJSkcFnjoOvz6WLtRitgjtDpdLBarTf1nLi4OJSUlNhvl5aWIi4uDgBgNBqRk5OD999/36HXio+PB9Cyq3TKlCk4evSorMWa1NwEaddWYOBQCFHOjy8REcmjySbi64Iq3NY5GLHBfnKHQ9fBNU83GDRoEAoLC3Hx4kU0NTXhyy+/tJ9MkJmZiYyMDAQEBNzwdRoaGlBXV2f//86dO5GcnOzW2G9E+u5boK4GmvHTZI2DiIic821RDWosNkxnuw6v57GVtaNHj2LVqlUQRRHp6emYNWtWq8crKyvx9ttvo76+HqIo4r777kNKSoqnwnMpnU6HpUuX4r777oMoipg9e7a9yNqwYQMeffTRVttXVFRgypQpqKurg0ajwYoVK7Bjxw4YjUY89NBDAFrOMJ01axbGjRvn8XyuaGnX8RXQqRtwywDZ4iAiIufY23WE+ePWWLbr8HYeKdZEUcTKlSvxwgsvIDIyEosWLUJqaiq6dOli3+azzz7DiBEjMHHiRBQXF+PVV19VbLEGAOnp6UhPT7/q/k8//fSq+2JiYnD48OGr7g8JCUFWVpZb4muXc2eAi+ch3D+fp3cTESnY2cuNOG+yYP5QtutQAo/sBi0oKEBcXBxiY2Oh0+kwcuRIHDx4sNU2giCgoaEBQMsuv4gILst6G2l7JhAYBGF4mtyhEBGREzLzTAjy0+D2HmzXoQQeWVkzGo2IjIy0346MjER+fn6rbe6++24sXboUW7ZsgcViweLFi6/5WllZWfbVpuXLlyMqKqrV4+Xl5dDpXJuWq1/P1fz9/a8ah/bS6XTXfC1bZQUqj+xD4Ix7ENK5yzWeqTxt5eqL1JSrK6hpvJir72or38t1Fuy7WIt7BndGlzhe19lRcv78eE0VsmfPHqSlpWHGjBnIy8vDm2++iddeew0aTevFv4yMDGRkZNhvV1ZWtnrcbDZDq9W6LK72nA3qaWaz+apxaK+oqKhrvpa4/mNAEmEeNg4WF72X3NrK1RepKVdXUNN4MVff1Va+Hx+7DFECxnX1V9V4OMsVPz8/7sF6MzyyG1Sv18NgMNhvGwyGqy6flJ2djREjRgAAkpKS0NzcjNra2pt+L41G4/XFlStZrdarClpXk5qbIH27FRhwG4ToOLe+FxERuU+TTcTW/Crc1oXtOpTEIytriYmJKC0tRUVFBfR6Pfbu3YsFCxa02iYqKgonT55EWloaiouL0dzcjNDQm9+XHhAQALPZDIvF4pKDJv39/WGxWJx+HXeQJAkajcahNiBOvc/Bb4HaamjSZ7j1fYiIyL12X6hFNdt1KI5HijWtVot58+Zh2bJlEEUR48aNQ9euXbF27VokJiYiNTUVDzzwAN577z1s3LgRADB/fvvOOBQEAR07dnRZ7GpbNv+plnYdG4H4rmzXQUSkYFfadXQN88MAtutQFI8ds5aSknJVK47Zs2fb/9+lSxcsWbLEU+GQo86dBS4UQPjFIzy9m4hIwXIrzThnNOPh22L5fa4wvIIBXZeUnQl0ZLsOIiKly8w1IqiDBmk9wuQOhW4SizVqk1RlgHRkL4TRGRACXLdrmYiIPMvQ0Iy9F2uRkRiGjh34q19p+IlRm6SdWwBRhDCO1wElIlKyLflVECVgahJPLFAiFmt0TVJzc0uxxnYdRESK1mwTsbWgCqmdgxEXwnYdSsRija5JOrS7pV3HeK6qEREp2e4Ltag2s12HkrFYo6tIkgRp+1ct7Tr6DJI7HCIiaqcr7Tq6hPphYBzbdSgVizW62vnclnYd46bx9G4iIgXLM5hRYDRjWnIEv88VjMUaXaWlXUcghBHj5A6FiIickHnWhMAOGoxjuw5FY7FGrdiMlyEd3gNhFNt1EBEp2eU6C/ZcrEE623UoHj89aqVx65c/tOuYKncoRETkhC9PlEGUgGls16F4LNbITmpuRuPW9UD/IRBiOskdDhERtVOzTcSXJ8swpFMQ4tmuQ/FYrJGddHg3xGoTNOkz5A6FiIicsOdiLYwNzZh+i17uUMgFWKyRnbQ9E9rO3YA+A+UOhYiInJCZa0K3iI5s1+EjWKwRAEA6nwsU5SNw6l0QNPyxICJSqtzKRuQbzPj5wHho2K7DJ/C3MgFoWVVDQEcEjJsidyhEROSEjbkmdNRpMKVPjNyhkIuwWCNIVUZ7uw5NxyC5wyEionYyNVqx52INMhLDEOSnkzscchEWawRp1xbAZoUwjtcBJSJSsq35VbCKwFS26/ApLNZUTrI2Q9q5Bbg1FUIs23UQESlVs03ClnwThnQKQqdQtuvwJSzWVE46tAeoqYJmPFfViIiUbO/FGpjMNkxP5qqar2GxpnJSdiYQ2xnoO1juUIiIyAmZuSZ0CvHDoHgee+xrWKypmFSYBxTmQRg3je06iIgULK+yEXkGM6Ylh7Ndhw/ib2gVk7Jb2nUII8fLHQoRETnhSruO8T3D5A6F3IDFmkpJ1SZIB3dDGJkOoSM7XBMRKZWp0YrdF2swPjEMgR20codDbsBiTaWkXVvZroOIyAdsLWhp1zGN7Tp8Fos1FWpp17EZ6J8CIa6z3OEQEVE7tbTrqEJKfBA6s12Hz2KxpkLS4b1AtQma8TPkDoWIiJyw7/tamBqtmMZ2HT6NxZoKSdmZQEwnoB/bdRARKVlmrgnxIR2Q0ontOnwZizWVkQrzgfO5EMZNZbsOIiIFyzc0IreyEdOSItiuw8fxt7XKSNmZgH9HCCPT5Q6FiIicsDHXhAC261AFFmsqItWYIB36FsLIcRACuWRORKRUVWYrvr1Qi/E9QxHkx3Ydvk7nqTc6evQoVq1aBVEUkZ6ejlmzZl21zd69e7Fu3ToIgoDu3bvj8ccf91R4qiDt2gpYrRDGTZc7FCIicsLX+VWwihLbdaiER4o1URSxcuVKvPDCC4iMjMSiRYuQmpqKLl262LcpLS3FF198gSVLliA4OBjV1dWeCE01JKsV0o4tQN/BEOK73PgJRETklayihM35VRgUH4QuYf5yh0Me4JHdoAUFBYiLi0NsbCx0Oh1GjhyJgwcPttpm+/btmDRpEoKDgwEAYWHcB+9K0pG9QLURmnSuqhERKdm+i7UwNloxg+06VMMjK2tGoxGRkZH225GRkcjPz2+1TUlJCQBg8eLFEEURd999NwYNGnTVa2VlZSErKwsAsHz5ckRFRbkvcAA6nc7t7+EJxm+3QozrjMi0Sdc9C9RX8nUEc6W2qGm8mKvybM2+hM5hAZg4oPt1zwL1lXy9hZzj6bFj1m5EFEWUlpbixRdfhNFoxIsvvoi//OUvCApqfSB8RkYGMjIy7LcrKyvdGldUVJTb38PdpAsFEM+egDD7IRiMxutu6wv5Ooq5UlvUNF7MVVkKDGacKK3FQ0NiYDQYrrutL+TrTVwxnp06dWrX8zyyG1Sv18Pwox8qg8EAvV5/1TapqanQ6XSIiYlBfHw8SktLPRGez5O2ZwL+ARBGZtx4YyIi8lob84wI0AlIZ7sOVfFIsZaYmIjS0lJUVFTAarVi7969SE1NbbXN0KFDcerUKQBATU0NSktLERsb64nwfJpUUwXp4C4II8azXQcRkYJVma3YVVSLcT3C2K5DZTyyG1Sr1WLevHlYtmwZRFHEuHHj0LVrV6xduxaJiYlITU3FwIEDcezYMTzxxBPQaDS4//77ERIS4onwfJr07dct7TrGT5M7FCIicsLXBT+06+CJBapzU8Xa5cuXceHCBZjN5lb3jx49+obPTUlJQUpKSqv7Zs+ebf+/IAj41a9+hV/96lc3ExJdR0u7jk1A30EQ4rvKHQ4REbWTVZSwJa8Kg+IC0ZXtOlTH4WJt/fr1+Oyzz9ClSxf4+fnZ7xcEwaFijTxPytkPVBmhuX++3KEQEZET9n9fC0OjFQ8P5eFBauRwsZaZmYnly5e3amRL3k3KzgSi44Bbh8gdChEROWFjrglxwR0wpFOw3KGQDBw+wSA4OBjR0dHujIVcSLp4Dig4DSFtKgQND0QlIlKq80YzTl9uxNSkCGg1bfdVI9/l8Mragw8+iPfeew/Tpk276uoCbLrnfaTsTMDPH8JotusgIlKyzFwT/LUC0hPZrkOtHC7WrFYrjh8/jj179lz12Nq1a10aFDlHqq2GdGAXhFHpEAK5ZE5EpFQ1Zit2FdUgPTEMwWzXoVoOF2vvv/8+5syZg1GjRrU6wYC8T0u7jmYI43kdUCIiJfu6oBrNbNeheg4Xa1f6o2muc11Jkp9ks0HasRnoMxBCp25yh0NERO1kEyVsyjdhQFwgurFdh6o5XHnNmDEDX3zxBSRJcmc85Kyj+wFTJTRcVSMiUrT9xbUwNFgxnatqqufwytrmzZtRVVWF9evXIzi49XFQf//7310eGLWPuP0rIDIGGJB6442JiMhrbcw1ISaoA1LZrkP1HC7WHnvsMXfGQS4gXTwP5J+GcPdctusgIlKwQpMZpyoaMTclmu06yPFirW/fvu6Mg1zA3q5j1AS5QyEiIidcadeR0TNc7lDIC9zUtUGLiopw5swZ1NbWtjp27cfX+CR5SLU1kL7bBWHEeAhBXDInIlKqGosNu4pqMK5HGIL9uZeEbqJYy8rKwocffogBAwbg6NGjGDRoEI4fP47UVB4b5Q2k3V8DzU0Qxk+TOxQiInLCtoIqNNnYroP+y+GzQb/88ks899xzePrpp+Hn54enn34aTz75JLRaVv1ya2nXsQm4ZQCEzt3lDoeIiNrJJkrYlGfCgNhAdA9nuw5q4XCxVlNTgz59+gAABEGAKIoYPHgwDh8+7LbgyEHHDgBGtusgIlK674rrUNlg5aoateLwblC9Xo+KigrExMQgPj4ehw4dQkhICHS6mzrsjdxA3J7Z0q5j4G1yh0JERE7IzDUiJkiH2zrz2GP6L4crrZkzZ+LSpUuIiYnBXXfdhddffx1WqxVz5851Z3x0A1JxIZB3EsJdD7JdBxGRghWZzDhZ0YhfDWa7DmrNoWJNkiT06dMHUVFRAIDBgwdj1apVsFqtCAgIcGuAdH1S9kbAzw/CaLbrICJSssxcE/y0AiYkhssdCnkZh45ZEwQB//M//wNB+G+lr9PpWKjJTKqrgXRgB4RhaRCCQuQOh4iI2qnGYsPOohqk9QhFCNt10E84fIJBQkICSktL3RkL3SRp9zagqQkCTywgIlK0rCvtOpJ4YgFdzeFj1vr164dXXnkFt99+u3136BXjx493eWB0fZLNBumbTUDyrRC6JMgdDhERtdOVdh39YwOREME9VnQ1h4u13NxcxMTE4MyZM1c9xmJNBse+A4yXoZn9a7kjISIiJ3x3qQ6XG6x4aEis3KGQl3K4WHvxxRfdGQfdJDE7E9BHAwOHyh0KERE5YWOuCdGBOgztwnYddG0OH7P2Y5IkQRRF+z/yLKm4CMg9AWHcVAi8ggQRkWIVmcw4Ud6AKUkRbNdBbXJ4Zc1oNGLlypU4c+YM6uvrWz22du1alwdGbZO+2Qh0YLsOIiKl25RX1dKuo1e43KGQF3N4Ze0f//gHdDod/vCHPyAgIAB//OMfkZqait/85jfujI9+QqqvhbT/GwjD0yAEh8odDhERtVOtxYZvCqsxNiEUoWzXQdfhcLGWl5eHRx55BAkJCRAEAQkJCXjkkUeQmZnpzvjoJ6TdWT+065gmdyhEROSErHMt7Tqm8zqgdAMOF2sajQbaH46PCgoKQk1NDfz9/WE0Gt0WHLUmibaWXaBJ/SF06SF3OERE1E4t7Tqq0C+mI3qwXQfdgMPHrPXq1Qs5OTkYOnQoBg4ciDfeeAN+fn5ITEx0Z3z0Y8cPAoYKaO6eJ3ckRETkhEOX6lBR34y5KdFyh0IK4HCx9thjj0GSJADAgw8+iK+++gpmsxlTp051W3DUmrg9E9BHAYOGyR0KERE5ITPXhKhAHYZ14aUC6cYcLtb8/f3x2WefYc+ePTCZTIiIiMDIkSMRFBTkzvjoB9Kli8DZ4xB+9gDbdRARKdjFKguOlzfgl4Oi2a6DHOJwsbZixQqUlJRg7ty5iI6OxuXLl7F+/XoYjUbMnz//hs8/evQoVq1aBVEUkZ6ejlmzZl1zu/379+P111/Hq6++yl2sPyJlZ/7QrmOi3KEQEZETMnNN6KARMDExTO5QSCEcLtYOHjyIN998076S1qVLF/Tu3RuPPfbYDZ8riiJWrlyJF154AZGRkVi0aBFSU1PRpUuXVts1NjZi8+bN6N27902m4duk+rqWdh1Dx0IIYbsOIiKlqrPYsKOwGrf3CEVogMO/gknlHD4bNDw8HBaLpdV9TU1NiIi48SnHBQUFiIuLQ2xsLHQ6HUaOHImDBw9etd3atWsxc+ZMdOjQwdGwVEHasw1oskAYP13uUIiIyAlZ56tgsUmYlsR2HeQ4h8v6sWPH4pVXXsHkyZMRGRkJg8GArVu3YuzYsTh58qR9u/79+1/1XKPRiMjISPvtyMhI5Ofnt9rm/PnzqKysREpKCjZs2NBmHFlZWcjKygIALF++HFFRUY6m0C46nc7t73E9ks0Gw66t0PQdCH2K+68DKne+nsRcqS1qGi/m6jk2UcLWgkIM6BSKoUldbvwEJ8mdr6+RczwdLta2bdsGAFi/fv1V9195TBAEvPXWWzcdhCiK+Ne//uXQsW8ZGRnIyMiw366srLzp97sZUVFRbn+P65GOHoBYXgJp1v0eiUPufD2JuVJb1DRezNVzDhTXoqTGgvsHRPL7XIFcMZ6dOnVq1/McLtbefvvtdr0BAOj1ehgMBvttg8EAvV5vv202m/H999/j5ZdfBgBUVVXhT3/6E5555hnVn2QgZmcCEVHAoOFyh0JERE7YmGtCZKAOw7qyXQfdHI8c3ZiYmIjS0lJUVFRAr9dj7969WLBggf3xwMBArFy50n77pZdewi9/+UvVF2pSyUXgzDEIs+6HoOOBqERESnWx2oJjZQ24f2AUdGzXQTfJIxWAVqvFvHnzsGzZMoiiiHHjxqFr165Yu3YtEhMTkZqa6okwFEf6ZiOg6wBh7CS5QyEiIidsutKuo1e43KGQAnlsuSYlJQUpKSmt7ps9e/Y1t33ppZc8EJF3kxrqIO3N/qFdB3vxEBEpVV2TDdnnqzEmIRRhbNdB7eBw6w7yLGnPdrbrICLyAdvPVcNikzA9me06qH1YrHkhSbS17ALt1QdCd3Uft0dEpGQ2UcKmPBP6RHdEoj5A7nBIoViseaMTR4DLZRDGz5A7EiIicsKRknqU1TVzVY2cwmLNC4nZXwHhkRAGs10HEZGSZeYaEdlRh+Fs10FOYLHmZaTS74HTRyGkTWG7DiIiBSuutuBoWQMmJ4WzXQc5hcWal5GyNwI6HYQxE+UOhYiInLAxzwQd23WQC7BY8yJSQz2kfdkQbhsLITRc7nCIiKid6n9o1zE2IQThbNdBTmKx5kWkvVmAxQwhne06iIiULPt8NcxWCdOS9DfemOgGWKx5CUkUIX2zCUi8BUL3XnKHQ0RE7SRKEjbmmXBLVEf0imS7DnIeizVvceoIUFHKJrhERAp3pKQepbXNmMZ2HeQiLNa8hLj9KyBcDyFlpNyhEBGREzJzTdB31GFkN7brINdgseYFpLJi4FQOhNsns10HEZGCFddYkFNaj8m92a6DXIfFmhewt+sYO0nuUIiIyAmbclvadUxiuw5yIRZrMpMaGyDtzYaQOgZCKI9vICJSqoZmG7afr8Ho7iEI78i9JOQ6LNZkJu3dDlgaeWIBEZHCbT9XDbNV5HVAyeVYrMlIEsWWXaA9kyH06C13OERE1E6iJGFTngnJUQHoHdlR7nDIx7BYk9OpHKCihKtqREQKl1NSj5LaZkxPZhNccj0WazISszOBsAgIQ9iug4hIyTbmmRARoMWIrmzXQa7HYk0mUtkl4ORhCLdPgaDrIHc4RETUTpdqmnC4pB6TkyLQQct2HeR6LNZkIu3YBGjZroOISOk25Zmg04DtOshtWKzJQGpsgLQnC8JtoyGE8awhIiKlami2Yfu5aozuFooItusgN2GxJgNpbzZgZrsOIiKl++Z8DRqtIq8DSm7FYs3DJFGE9M1GoEcShB5JcodDRETtJEoSMnNNSIoMQFIU23WQ+7BY87TTOUD5Ja6qEREp3NHSepTUNnFVjdyOxZqHidkbW9p1pI6SOxQiInJCZm5Lu45R3ULlDoV8HIs1D5LKS4AThyCMncR2HUREClbyQ7uOSb3D2a6D3I7FmgdJ32z8oV3HZLlDISIiJ9jbdfTmLlByPxZrHiKZf2jXMWQUhHBejoSISKkamm3Yfr4aI7uFQs92HeQBLNY8RNr3TUu7jnSeWEBEpGTfnK9BQ7OI6TyxgDyExZoHSKIIKTsTSOgNoWey3OEQEVE7iZKETXkm9I4MQDLbdZCHeGz99ujRo1i1ahVEUUR6ejpmzZrV6vHMzExs374dWq0WoaGheOSRRxAdHe2p8NzrzDGg7BKEh56QOxIiInLCsbIGFNc04YmR8XKHQirikZU1URSxcuVKPPfcc3jjjTewZ88eFBcXt9omISEBy5cvx1/+8hcMHz4cq1ev9kRoHiFu/woICYMwZLTcoRARkRM25hoRHqDFqG4hcodCKuKRYq2goABxcXGIjY2FTqfDyJEjcfDgwVbb9O/fH/7+/gCA3r17w2g0eiI0t5MqSoCThyHcPgVCB7brICJSqtLaJhy6dKVdB48iIs/xyG5Qo9GIyMhI++3IyEjk5+e3uX12djYGDRp0zceysrKQlZUFAFi+fDmioqJcGutP6XQ6p96jdsMaNGg0iLxzDrR698bqCs7mqyTMldqipvFiro5bc+o8NBoBc4b2RFSwvwsjcw81fbaeIOd4et05x7t27cL58+fx0ksvXfPxjIwMZGRk2G9XVla6NZ6oqKh2v4dkboSY9RWEIaNgEgXAzbG6gjP5Kg1zpbaoabyYq2Mam0VknirDqK4hEMy1qDTXujg611PTZ+sJrhjPTp06tet5HlnH1ev1MBgM9tsGgwF6/dW9xo4fP47169fjmWeeQQcf2GUo7fsGaGzgdUCJiBRuR2E1GppFXgeUZOGRYi0xMRGlpaWoqKiA1WrF3r17kZqa2mqbwsJCrFixAs888wzCwsI8EZZbSZLUcsWC7r0AtusgIlIsSZKQmWtCL30AkqMC5A6HVMgju0G1Wi3mzZuHZcuWQRRFjBs3Dl27dsXatWuRmJiI1NRUrF69GmazGa+//jqAluXGZ5991hPhuceZY0Dp9xDmLoQg8LpxRERKdaVdx+Mj4vl9TrLw2DFrKSkpSElJaXXf7Nmz7f9fvHixp0LxCDE7s6Vdx21j5A6FiIickJlrQliAFmO6s10HyYPnHruBdLkMOH4QwthJbNdBRKRgZbVNOHSpDpN6sV0HyYc/eW4gfbMR0Ggg3D5F7lCIiMgJm/JM0AjA5N7hcodCKsZizcUkcyOk3VkQUkZCiIi88ROIiMgrNTaLyDpXjRHdQhAZyL0kJB8Way4m7d8BNNazXQcRkcLtKKxGfbOI6WzXQTJjseZCkiRBys4EuiUCibfIHQ4REbWTJEnYmGdCot4ft0R1lDscUjkWa6509nhLu4706Ty9m4hIwY6XN+D76iZMT9bz+5xkx2LNhcTsTCA4lO06iIgUbmOuCaH+Woxmuw7yAizWXES6XAYc+w7C2MkQOvjJHQ4REbVTeV0Tvituadfhx3Yd5AX4U+gi0o7NgCBAuH2y3KEQEZETNuVVQRCAyUnhcodCBIDFmktIFjOk3V+3tOvQR8kdDhERtZPZKmLbuSqM6BqCKLbrIC/BYs0FpAM7gAa26yAiUrqdhTWob2K7DvIuLNac1NKuYyPQrSfQq4/c4RARUTtJkoTMXCN6RvijTzTbdZD3YLHmrNwTwKULEMazXQcRkZKdKG/AxeomTEuO4Pc5eRUWa06yt+sYOlbuUIiIyAmZP7TrGJsQKncoRK2wWHOCZKgAjn4HYcxEtusgIlKw8romHLxUh4ls10FeiD+RTpC+2QQIgJA2Re5QiIjICZvzqgAAk3uHyxoH0bWwWGsnyWKB9O3XwODhEPTRcodDRETtZPmhXcfwriGIDmK7DvI+LNbaqaVdRx0042fIHQoRETlhZ1EN6tiug7wYi7V2aGnXkQl06QH07it3OERE1E4t7TpM6BHhj75s10FeisVae+SdbGnXkc52HURESnayogEXqiyYznYd5MVYrLVDS7uOELbrICJSuI25JoT4azGmO9t1kPdisXaTJEMFkHOgpV2Hn7/c4RARUTtV1DXjQHEdJiaGwV/HX4fkvfjTeZOkHZsBAMLtU2WOhIiInLE53wQAmJLEEwvIu7FYuwlS04/adUSyXQcRkVJZrCK2FVRhWBe26yDvx2LtJkgHdgL1tdCMny53KERE5ISdRTWoZbsOUggWaw76b7uOBCCpn9zhEBFRO0mShI25JiSE+6NfDNt1kPdjseao/FNAcRGE8WzXQUSkZKcqGlFUZcE0tusghWCx5iBxeyYQFAJh6O1yh0JERE7IzDUhxE+D2xPYroOUgcWaAyTDZeDofgijJ0DwZ7sOIiKlulzfjAPFtZjQK5ztOkgx+JPqAGnnJkAChHFs10FEpGSb835o19GbJxaQcrBYuwHJYoG062tg8DAIkTFyh0NERO1ksdrwdUEVhnUJRkww23WQcug89UZHjx7FqlWrIIoi0tPTMWvWrFaPNzc346233sL58+cREhKChQsXIiZG/uLI/O02tusgIvIB23Ivo7ZJxDS26yCF8cjKmiiKWLlyJZ577jm88cYb2LNnD4qLi1ttk52djaCgILz55puYNm0a1qxZ44nQrkuSJDRsXAd07g4k9Zc7HCIiaidJkrDuaCm6h/ujf0yg3OEQ3RSPFGsFBQWIi4tDbGwsdDodRo4ciYMHD7ba5tChQ0hLSwMADB8+HCdPnoQkSZ4Ir235p2Etyme7DiIihTt9uREFlfWYznYdpEAe2Q1qNBoRGRlpvx0ZGYn8/Pw2t9FqtQgMDERtbS1CQ1ufWp2VlYWsrCwAwPLlyxEVFeW2uJvrOqMxbQpCpv0cgn+A297Hm+h0OreOqTdhrtQWNY2XWnLtLNVjch8LfjakBwI6aOUOxyPU8tl6ipzj6bFj1lwlIyMDGRkZ9tuVlZXue7PgcEQ9vrjlPWrr3Pc+XiQqKsq9Y+pFmCu1RU3jpZZcwwVg8cTeqKyshDq+zdXz2XqKK8azU6dO7XqeR3aD6vV6GAwG+22DwQC9Xt/mNjabDQ0NDQgJCfFEeEREREReyyPFWmJiIkpLS1FRUQGr1Yq9e/ciNTW11TZDhgzBjh07AAD79+9Hv379eFwBERERqZ5HdoNqtVrMmzcPy5YtgyiKGDduHLp27Yq1a9ciMTERqampGD9+PN566y089thjCA4OxsKFCz0RGhEREZFX89gxaykpKUhJSWl13+zZs+3/9/Pzw5NPPumpcIiIiIgUgVcwICIiIvJiLNaIiIiIvBiLNSIiIiIvxmKNiIiIyIsJkuzXdCIiIiKitnBl7Qb+3//7f3KH4FFqype5UlvUNF7M1XepLV93k3M8WawREREReTEWa0RERERejMXaDfz4ovFqoKZ8mSu1RU3jxVx9l9rydTc5x5MnGBARERF5Ma6sEREREXkxFmtEREREXozFGrkc96z7Jn6udD38+fBN/Fy9A4s1NxJFUe4QPKayshJlZWUAAEEQfHqC5+Xl4ejRo3KH4RE1NTVoaGgA0PK5qulnuj3UNj6c976J89776OQOwFedPHkSRqMRKSkpCA4Oljsctzpy5AjWrFkDvV4PQRDw3HPP2b+4BUGQOzyXOnr0KD7++GP87ne/a3W/L+aak5OD//znP4iOjoYkSXjqqaeg0fDvu7aoac4DnPcA5z055vTp09BoNEhKSmr3WPITcIOzZ89iyZIl2LlzJ44fP466ujq5Q3KbwsJC+5fY888/j8DAQNTX1wOAz32JnTp1Cm+++SYeeeQRJCYmwmKx2FcSfG1V4eTJk/jwww8xZ84cPPzwwzCbzfjiiy/kDstrqWnOA5z3nPfkqNOnT+Pll1/GRx99hIKCgnavUnJlzcVsNhvq6+uxcOFCCIKAgwcPQhRFDBo0yP7Xti/9NSYIAvr164ekpCQYDAacOnUKH330EWpqavD444/D39/fJ/KVJAm1tbWIiYmBzWaD2WzG22+/DT8/P5jNZjz++OPw8/NTfK6SJMFqtaK4uBj33XcfBgwYAAAYP348ioqK5A3OS6ltzgOc95z35Air1YqysjI89dRTMJlM+PTTT/Gzn/2sXSts7LPmBk1NTRBFEQEBAfj2229x7NgxDBw4EIMHD/a53SOXLl3CP//5T8THx+PIkSPIyMjA5MmT8c4776CxsRHPP/+83CG6jNlsxsmTJ7Fp0yZcuHABd955J4YNG4YPP/wQTU1NeO655+QO0WWqqqogiiIiIiIgCALOnDmDdevW4Q9/+IPcoXklNc15gPOe854cVVNTAz8/PwQEBGDDhg04deoUZs2aheTk5Jsq2Liy5iKnT5/G2bNn0aNHD8THxyMuLg4AMGbMGEiShGPHjiEsLAyFhYWor6/HfffdJ3PE7ffjXHv37o2HH34YJpMJdXV1mDRpEgICAvDkk0/i1VdfRU1NDUJDQ+UOud0uX76M6OhoAEBAQAD69++PpqYm1NbWYtKkSQCAJ554Aq+99hoaGhoQGBgoZ7hOOXv2LPLz89GpUyf06NEDer3e/tiVlQQA2L59OyorKzF79my5QvUKaprzAOc95z3nvaPy8vJw8eJF9OrVC3q9HgEBAQCAO+64AwDwxRdfYO7cuThz5gzq6+sxffr0G74mj1lzgePHj+Pvf/87LBYLjh8/jvfeew+nTp2yPz527FiMHTsWH330EbZs2YIRI0bIGK1zfpzrsWPH8Nprr6GsrAy9evWCv78/zp8/DwDYu3cvqqurodMp9++BQ4cO4fe//z0+/fRT+30BAQG47bbbMGHCBPt9u3fvRm1traJ3gxw+fBgrVqyAwWDAwYMHkZ2djaamJvvxOLGxsejevTv27duHrKwsDB8+XOaI5aWmOQ9w3nPec9476siRI/jrX/+KwsJCZGZmYvXq1Th37pz98TvuuAMjRozAkiVLsG7dOvsu5xtR7ozyIsXFxcjIyMDMmTPR0NCAQ4cOYeXKlXjooYfQr18/AC1L6eXl5XjllVfQpUsXmSNuv7ZyXbBgAVJTU/HBBx8gISEB33//PX7/+98r9i/O6upq7N+/H/feey/2798PrVaLO++8EwDQoUMHAC1tGnbv3o0NGzZg4cKF6Nixo5wht1tpaSnWrVuHRx55BL169cKJEyfwxRdfYPLkyfDz8wPQckxLdnY2zp49i6eeekrRP8OuoKY5D3DeA5z3nPeOKSgowL333osxY8agpKQEx48fx3/+8x/Mnj0bPXv2BAAEBgairq4Oy5Ytc3hMWay5gJ+fn/0vy8DAQIwdOxZAy1JnVFQUYmNjERAQ4BNf2tfKVZIkrFmzBk8//TQWLlwIm82G4OBgREVFyRxt+4WGhmLChAlITk7G8OHD8ac//QkA7F/cANDY2AiDwYAnnnhC0Z9rTEwMpk6dim7dugEAbr31VmzevBlFRUXo378/gJZfVCNGjMA999yDTp06yRmuV1DTnAc47znvOe8dZbVacerUKYwZMwadOnWyH7OalZWFe++9FwEBAWhoaMDSpUtv6ueHu0FdIC0tDRcuXMC//vUv+30pKSmIi4uD0WgE0DIRlDyxr7hWrkOGDEFMTAzOnTuHrl27IiEhQdFf2FfO7EpOTgYAxMfH45lnnsGuXbvw+eefA2jZLWSxWDBz5kxFf66iKEKr1WLs2LHw8/Ozn1au0Wjsx6oUFBQAAObPn88v7B+oac4DnPec95z3jpo5cyZKS0uxYcMGAC1/APTt2xcNDQ2oq6uDn58fxo4di65du97U67JYc5IoitDpdFi0aBEKCgrw4YcfAgCCg4Nhs9nsf40q+biGK66XqyRJ9lyV7qeflc1ms39xHzhwAMuWLcOqVatgs9kU3yzyp/Ff+dKOjIxEREQEDh06hDVr1qCxsdG+a0StroyNWua8I/n6yrwXRVE1814URc57NxFFEUFBQfjFL36B3Nxce4+6bt26QaPR2I9da8/PD1t33KTKykoEBwfbz+4AWia1VqtFVVUV/vSnPyE+Ph6hoaE4cuQInn32WcX+VaL2XK/VO+k///kPtm7dihdffNG++0BpHMn1008/xXfffQedToeHH35Ysbm6QlFREcLDwxEeHm6/z1fnAaCufK+Vq6/Oe0dy5bxvv58WwQUFBVizZg2io6MRHx+P7OxsLF68GDExMe16feX+eSCDgwcPYsWKFSgvL7ffd2U5OT8/H/n5+ViyZAmGDBmCzp074+mnn1bslxhzbflL+9y5c8jOzgYAlJWV4dKlS1i8eLFiv8RulGtWVhYAoKGhAcXFxViwYIFic3WFY8eO4Y9//CN27doFoGWsfHUeAOrK91q5XilefG3e3yhXzvubd/z4caxevRpffPEFDAYDNBqNfYXy4sWLqK+vx7PPPovOnTtDq9Xi6aefbnehBrBYc9iFCxewZs0a3Hnnnejevbv9fo1GgzNnzuAf//gHOnToAK1Wi5EjRyIjI0OxxzQw1//m+u6779r7LcXGxuJ3v/sdEhISZIrWOY7keuXLZM6cOfjLX/5i7x2mRseOHcPHH3+MAQMGoLCwEEDLWGk0GuTl5fnUPADUlW9buQqCgLy8PJ+a947kynl/c44cOYLVq1cjLCwMlZWVyMnJAdAyridOnMBf//pX6HQ6BAQEYObMmbjjjjucLn5ZrDmouroavXv3xi233ILKykps3rwZGzduRFlZGSRJwuzZszFo0CCfuE4cc22d66233mpffVJqSwLAsVwHDBgAq9WKDh06KHbFxBXOnj2LlStX4re//S0eeeQRlJWVteq7VV5ejjlz5vjMPFBTvo7m6gvz3tFcOe8dJ4oiDh06hF/84heYMWMGunfvjrKyMpw6dQpGoxENDQ34+c9/jn79+rl0rrBYc1BoaCj8/f1hNpvx1ltvwWAwwGAw4MUXX0RERARSU1MVf324K5jr1bkq+YDiKxzNVckNTV0lLi4OCxcuRGJiIgDgrrvuQlVVlf1i5WPGjEFKSgoA5Z9IAKgr35vJVenz/mZy5bx3jCRJaGhowPHjx1FUVITMzEwYDAbs27cPf/7zn5GYmIiRI0e6/HckTzC4jqqqKmg0GoSGhqKxsRGvvvqq/TTcqVOnAmg5ILO5uRlz5syROVrnMFfmqvRcXaGqqgqSJCEiIqLV/RUVFXjjjTcwffp0jBo1SqboXE9N+TJX38zVU6qqqgAA4eHhuHz5Mv75z39Cq9UiIiICc+fOBQB89NFHiI6OxuTJk13+/iyl27B//35s2rQJNpsNqampGDx4MP7nf/4HixcvRn19PSZNmgStVgt/f380NzfLHa5TmCtzVXqurvDj8Ro6dCgSEhIwcOBAAC0NRGfOnInNmzcjOTlZ0f3ErlBTvszVN3P1lB+P6ZAhQzBo0CA8/fTT2L9/P8rKyuzbCYKAhoYGt8Sg7DVeN6mtrcXnn3+OuXPn4ne/+x06dOiAzZs3o7i4GEuWLIHFYsG6deuwevVq7N69G6NHj5Y75HZjrsxV6bm6wk/HS6vV4sCBA9izZ499m759+yIkJKTVl7NSqSlf5uqbuXrKT8fUz88P27Ztw6FDh9C3b1/s2bMH//73v7F9+3acPHkSw4YNc0scXFm7BlEU0bFjR8TExCAoKAghISE4ceIEdu3ahWnTpmHRokU4f/48DAYDxo8fr+gDMpkrc1V6rq7Q1nidPn0aYWFh6N+/P0JDQ5GcnIzY2Fi5w3WamvJlrr6Zq6e0NaaHDx9GeHg4FixYgE8//RS1tbWYP38+Onfu7JY4eMxaG1atWgWLxYIHH3wQAQEBMBqN2LVrFzQaDe644w65w3Mp5spcqe3x0ul0mD59utzhuZya8mWuvpmrp9zou9Rms0EQBLeekMLdoD9xpandpEmT0LFjR3z55Zcwm83Q6/UYOHAgDh8+jLq6OpmjdA3mylzpxuN18OBBnxovNeXLXH0zV09x9LtUq9W6/cxhFms/cWXA4+LiMGzYMDQ1NWHFihWoqalBaWmpRz4UT2GuzJXUN15qype5+maunuJNY8rdoD/48XW9rvzfYDCgrq4OO3fuRHFxMerq6vDrX/8aPXv2lDla5zBX5qr0XF1BbeOlpnyZq2/m6ineOKaqLtaOHTuG8+fP48477wTQ+gM6efIkvv76azzwwAOIiopCQ0MDNBpNq4tfKwlzZa5Kz9UV1DZeasqXufpmrp7i7WOq2rNBT58+jTfffBPBwcGora3FAw88YL8Qa0NDAz7++GPMnDnT3odGqZcbAZgrc1V+rq6gtvFSU77M1Tdz9RQljKlqV9Z2794Ns9mMYcOG4Y033kBCQgIeeOAB++MmkwkRERGtqmulYq7MVem5uoLaxktN+TJX38zVU5Qwpqot1gCgpqYGoaGhqKiowHvvvYeuXbviwQcfBADU19cjKChI3gBdiLk+CIC5qp3axktN+TLXBwH4Xq6e4u1jqqpi7ezZs6iurkZzc7O9Y/uVi62Wl5fjH//4B/r06YMuXbqgrKwM06dPV+zFbZkrc1V6rq6gtvFSU77M1Tdz9RSljalqirUjR45g9erVGDlyJPbu3YuUlBTcf//9rbaxWq149NFH0dTUhJdffhndunWTKVrnMFfmqvRcXUFt46WmfJmrb+bqKYocU0kFSkpKpGeffVY6c+aMJEmSVF5eLv3xj3+UqqurJVEU7dvt27dPmj9/vnTx4kW5QnUac2WuSs/VFdQ2XmrKl7n6Zq6eotQxVc3Rh3fccQduueUW+3W+qqurUV1dDUEQIP2wuGg2m7Fo0SJ07dpV5midw1yZq9JzdQW1jZea8mWuvpmrpyhxTH16p3ZlZSXCw8MRHR2N+Ph4AIAgCAgJCUFsbCz8/f0BABcuXEBCQgLS0tJkjNY5zJW5Kj1XV1DbeKkpX+bqm7l6itLH1GeLtSNHjmDNmjXo3bs3Ghsbcc8996Bz586w2WzQ6XSor6+H2WzGrl278Omnn2LJkiUIDQ2FIAhyh37TmCtzVXqurqC28VJTvszVN3P1FF8YU5/bDSpJEiorK7FmzRrMmzcP9957L3r16oWXX34Z33//vf1sjrCwMKxbtw7btm3D008/jbCwMK/6YBzBXJmr0nN1BbWNl5ryZa6+maun+NKY+lyxJggC9Ho9kpKSEB8fj7CwMMyYMQOzZs3C0qVLUVJSAgAIDg5GUVERHn74Ya/ZJ32zmCtzVXqurqC28VJTvszVN3P1FF8aU58q1srKylBQUID6+no0NDTg22+/tVfHU6dOxdSpU/H5559DFEXceuuteOGFF9C5c2eZo24f5spclZ6rK6htvNSUL3P1zVw9xdfG1GeOWTt8+DA++eQTBAUFoVu3bhgzZgxWrVoFURTtF2YdMWIESktLodFoMHjwYJkjbj/mylyVnqsrqG281JQvc/XNXD3FF8fUJ1bWcnNzsXr1ajz66KN4+eWXYbVaUVBQgCVLluDrr7/GZ599hrKyMpw+fRpFRUWoq6uTO+R2Y67MVem5uoLaxktN+TJX38zVU3x1TH3iCga5ubkoLS21n2pbU1ODt99+G4sWLUJ5eTk+++wz+Pn5IT8/H48++qj8nYidwFyZq9JzdQW1jZea8mWuvpmrp/jqmPrEbtDevXvbDwoURRHNzc0wmUwwmUyIjY3F3XffDb1eD4vFgsDAQJmjdQ5zZa5Kz9UV1DZeasqXufpmrp7iq2PqE7tBNRqNfdAlSUJQUBCCg4MRERGBXbt2Yf369bDZbIr6YNrCXJkrqW+81JQvc/XNXD3FV8fUJ4q1H9NqtQgICEBkZCQ+/vhjbNy4EZMnT4afn5/cobkcc2WupL7xUlO+zNU3c/UUXxpTn9gN+mOSJMFms+Hs2bOwWq34wx/+YL+0hK9hrsyV1DdeasqXufpmrp7iS2PqEycYXMuOHTuQmJjotQ3uXIm5+iY15eoKahsvNeXLXMkZvjCmPlusSZLkdZeLcBfm6pvUlKsrqG281JQvcyVn+MKY+myxRkREROQLfO4EAyIiIiJfwmKNiIiIyIuxWCMiIiLyYizWiIiIiLwYizUiUp23334b//73v+UOg4jIISzWiIja8NJLL2H79u1yh0FEKsdijYiIiMiL+dzlpoiIfqqwsBDvvvsuSktLMXjwYHuDzLq6Orz11lvIz8+HKIpITk7Gb37zG0RGRuKTTz7BmTNnkJ+fj3/+859IS0vDQw89hEuXLuGDDz7A+fPnERoaitmzZ2PkyJEyZ0hEvowra0Tk06xWK/785z9jzJgx+OCDDzBixAgcOHAAQEtn87S0NLzzzjt455134Ofnh5UrVwIA5syZgz59+mDevHn46KOP8NBDD8FsNmPp0qUYPXo03n//fSxcuBArV65EcXGxnCkSkY9jsUZEPi0vLw82mw3Tpk2DTqfD8OHDkZiYCAAICQnB8OHD4e/vj44dO+JnP/sZzpw50+ZrHTlyBNHR0Rg3bhy0Wi169OiBYcOGYd++fZ5Kh4hUiLtBicinmUwm6PX6VtcGjIqKAgBYLBZ8+OGHOHr0KOrr6wEAjY2NEEURGs3Vf8tevnwZ+fn5ePDBB+332Ww2jB071r1JEJGqsVgjIp8WEREBo9HY6mLOBoMBcXFx+Oqrr1BSUoJXXnkF4eHhKCoqwjPPPIMrl0z+6cWfIyMj0bdvXyxevNjjeRCRenE3KBH5tKSkJGg0GmzevBlWqxUHDhxAQUEBAMBsNsPPzw+BgYGoq6vDunXrWj03LCwM5eXl9ttDhgxBaWkpdu3aBavVCqvVioKCAh6zRkRuJUhX/oQkIvJR586dw3vvvYeysjIMHjwYABAfH4+JEyfib3/7G86dOwe9Xo/p06djxYoV+OSTT6DVapGXl4e3334bNTU1GDNmDObNm4eSkhJ8+OGHKCgogCRJ6N69O371q18hISFB3iSJyGexWCMiIiLyYtwNSkREROTFWKwREREReTEWa0RERERejMUaERERkRdjsUZERETkxVisEREREXkxFmtEREREXozFGhEREZEX+/8Cr/IlLLVIugAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "effects = (twfe_model.params[twfe_model.params.index.str.contains(\"treat\")]\n",
    "           .reset_index()\n",
    "           .rename(columns={0:\"param\"})\n",
    "           .assign(cohort=lambda d: d[\"index\"].str.extract(r':cohort_(.*):'),\n",
    "                   date_0601=lambda d: d[\"index\"].str.extract(r':C\\(date_0601\\)\\[(.*)\\]'),\n",
    "                   date_0715=lambda d: d[\"index\"].str.extract(r':C\\(date_0715\\)\\[(.*)\\]'))\n",
    "           .assign(date=lambda d: pd.to_datetime(d[\"date_0601\"].combine_first(d[\"date_0715\"]), errors=\"coerce\")))\n",
    "\n",
    "           \n",
    "plt.figure(figsize=(10,4))\n",
    "sns.lineplot(data=effects.dropna(subset=[\"date\"]), x=\"date\", y=\"param\", hue=\"cohort\")\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ea1018",
   "metadata": {},
   "source": [
    "Notice that we could go even further, since the effect for both cohorts follow the same shape. Namely, we could restrict the model to have the same effect for both cohorts, changing only over time. To do that, we would need to create a column to represent days after the treatment, just like in the event study design. It would be something like this:\n",
    "```\n",
    "days_after_treat=1(date>cohort)*(date - cohort)\n",
    "```\n",
    "Then, we would interact it with the treatment indicator \n",
    "```\n",
    "installs ~ treat:C(days_after_treat) + C(unit) + C(date)\n",
    "```\n",
    "However, I think we can stop here. Not allowing heterogeneity by cohort is, more often than not, a bad idea, since treatment effect tends to change over calendar time, not only over time since the treatment. For example, it could be that, after some time, competitors copy our feature making it no longer a strong customer attractor as it once was. In that case, the effect of the new feature on installs would diminish over time. \n",
    " \n",
    "One last thing we should do besides showing the effects over time is to plot the counterfactuals to see if they are in a place that feels right. I know this is not a very scientific validation of our model, but trust me, it helps a tone. So here it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cab314ce",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "twfe_model_wrong = smf.ols(\"installs ~ treat + C(date) + C(unit)\",\n",
    "                           data=df_pred).fit()\n",
    "\n",
    "\n",
    "df_pred = (df_pred\n",
    "           .assign(**{\"installs_hat_0_wrong\": twfe_model_wrong.predict(df_pred.assign(**{\"treat\":0}))}))\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "sns.lineplot(\n",
    "    data=(df_pred\n",
    "          [(df_pred[\"cohort\"].astype(str) > \"2021-06-01\") & (df_pred[\"date\"].astype(str) >= \"2021-06-01\")]\n",
    "          .groupby([\"date\"])[\"installs_hat_0\"]\n",
    "          .mean()\n",
    "          .reset_index()),\n",
    "    x=\"date\",\n",
    "    y = \"installs_hat_0\",\n",
    "    ls=\"dotted\",\n",
    "    color=\"C3\",\n",
    "    label=\"counterfactual\",\n",
    ")\n",
    "\n",
    "sns.lineplot(\n",
    "    data=(df_pred\n",
    "          .groupby([\"cohort\", \"date\"])[\"installs\"]\n",
    "          .mean()\n",
    "          .reset_index()),\n",
    "    x=\"date\",\n",
    "    y = \"installs\",\n",
    "    hue=\"cohort\",\n",
    "    legend=None\n",
    ")\n",
    "\n",
    "plt.ylabel(\"Installs\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b82806",
   "metadata": {},
   "source": [
    "As we can see, the counterfactual $Y_0$ prediction falls right where we think it should fall, that is, pretty close to the control cohort. This is very comforting. We know that the TWFE model is estimating the treatment effect as $Y-\\hat{Y_0}$. That is, it is simply comparing the outcome from the treated cohorts to that counterfactual. Since the counterfactual seems OK, we can rest assured that the treatment effect is also probably OK. \n",
    " \n",
    "That is the good news, but don’t think I forgot about the bad news I’ve promised you. The thing is that, although we’ve solved the functional form problem with TWFE, there is still an arguably bigger problem with DiD and TWFE, which is related to its independence assumption. \n",
    " \n",
    "When using DiD and TWFE, we often invoke the parallel trends assumption, without really thinking about what exactly that assumption means. Sadly, the parallel trends assumption is much more restrictive and less plausible than most people realize\t. But since this chapter is already too big, I think it's fine to end it here, where we can still enjoy the taste of a small victory for DiD. \n",
    " \n",
    "## Key Concepts\n",
    "\n",
    "I think it is safe to say that we've finally managed not only to understand, but to correct the functional form problem with TWFE. We traced the problem down to its roots (time heterogeneity) and fixed it by allowing for more flexibility. We can now grab ourselves a drink and relax, knowing that TWFE is once again safe to use. Or is it?!\n",
    " \n",
    "![img](data/img/did-saga/twfeworking.png) \n",
    "\n",
    "We can never forget that TWFE (and DiD more generally) is a mix of **both functional form and independence assumptions**. In this chapter, we've only tackled the functional form problems, but there is still a big elephant in the room: the parallel trend assumption. Parallel trends is the independence assumption DiD makes. This is very well known. But I feel we don't really understand what this assumption implies. We just invoke it out of thin air as if that would make it true. Unfortunately, the parallel trend assumption requires much more than what most people realize. In the next chapters, we will see why that is and what or if we can do something about it.\n",
    " \n",
    "## Reference\n",
    "\n",
    "\n",
    "It took a (very) long time to write this chapter. The recent econometric literature has been sprouting with new ideas and insights on the problems related to DiD and how to solve them. There are multiple angles we can see those problems, which in turn leads to multiple approaches we can leverage to solve them. Brace yourselves because the reference list here will be long (and probably not very well organized).\n",
    " \n",
    "First, I took a lot from *Difference-in-Differences with Variation in Treatment Timing*, by Andrew Goodman-Bacon. His diagnosis of the problem is very neat and intuitive. Not to mention it comes with very nice pictures that gave me a clear understanding on how to understand what was happening with DiD. Some of the pictures in this chapter are almost a direct copy from those made by Goodman-Bacon.\n",
    " \n",
    "The second major source of inspiration was *Difference-in-Differences with Multiple Time Periods*, by Pedro H. C. Sant'Anna and Brantly Callaway. Notice that the Callaway and Sant'Anna solution to this problem goes on a different route than the one we took. Still, their solution shines a lot of light on the DiD problem, making it easy to understand. The paper aside, Pedro has a very nice blog post showing the problems with TWFE. The Data Generating Process on that blog post heavily inspired the ones that I've used here. I’ve basically translated Pedro’s code from R to Python. Pedro was also very kind in helping me with some questions I had about the DiD assumptions. He has another very interesting article tackling the problem of adding covariates to the DiD models, which we didn’t cover here because the chapter was already too long. The name of the paper is *Doubly Robust Difference-in-Differences Estimators* and I strongly recommend you read it if you plan to add covariates to your model. \n",
    " \n",
    "Last, but definitely not least, the functional form fix we employed here was inspired on *Estimating dynamic treatment effects in event studies with heterogeneous treatment effects*, by Sun and Abraham and on *Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators*, by Jeffrey Wooldridge. Although I've loved what Callaway and Sant'Anna did on their paper, their solution is a bit more complicated to implement. In contrast, Sun, Abraham and Wooldridge's solution involves nothing more than cleverly playing with interactions in a TWFE regression model, making it super easy to implement using nothing more than `statsmodels` and some formulas. \n",
    " \n",
    "Aside from the papers mentioned above, I've also relied on presentations given at the DiD Study Group, organized by Taylor Wright on Youtube. It is much easier to understand the articles mentioned above if you first watch the authors talk about them. I'm also very thankful for professor Yiqing Xu for tying all of this together in his amazing Causal Inference with Panel Data lecture series, also available on Youtube. \n",
    "\n",
    "Finally, keep in mind that I too am also learning, so, by all means, if you find something preposterous, open an issue and I'll address it to the best of my efforts.\n",
    "\n",
    "\n",
    "## Contribute\n",
    " \n",
    "Causal Inference for the Brave and True is an open-source material on causal inference, the statistics of science. It uses only free software, based in Python. Its goal is to be accessible monetarily and intellectually.\n",
    "If you found this book valuable and you want to support it, please go to [Patreon](https://www.patreon.com/causal_inference_for_the_brave_and_true). If you are not ready to contribute financially, you can also help by fixing typos, suggesting edits or giving feedback on passages you didn't understand. Just go to the book's repository and [open an issue](https://github.com/matheusfacure/python-causality-handbook/issues). Finally, if you liked this content, please share it with others who might find it useful and give it a [star on GitHub](https://github.com/matheusfacure/python-causality-handbook/stargazers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7aa8d723",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
